Why state policies matter: the influence of curriculum policy on participation in post-compulsory education and training.
The large growth in school completion rates over the past two decades in Australia has been well documented. What is less frequently reported is that the relative completion rates of the different states and territories diverged over that time. Whereas at the beginning of the 1980s the five mainland states all had quite similar rates of school completion, by 1990 substantial gaps had emerged (Lamb, 1998). Although there has been some convergence during the 1990s, large gaps remain (Australian Bureau of Statistics (ABS), 2002). Even more interesting than the gaps themselves is that different patterns of post-compulsory participation developed over that time. Some states and territories experienced much larger rates of growth and change than others. Our thesis is that these patterns reflect underlying differences in state curriculum policies. In this paper, we show that state differences in organisation and policy in post-compulsory education and training have substantial effects on patterns of participation. Furthermore the effects of state policy are not uniform for all groups of young people. The chances of surviving in education and training for students from lower socioeconomic backgrounds vary across states. Some states have proven more effective than others in promoting high levels of participation and supporting greater equity across social groups.
Support for the view that state curriculum policy and organisation influence participation in post-compulsory education and training comes from earlier research based on longitudinaal data, covering several student cohorts from 1977 to 1991 (Collins & Vickers, 1999; Vickers, 1995). Using data from the Australian Longitudinal Survey (ALS) and the Australian Youth Survey (AYS), that work shows that, in states where formal assessments were imposed at the end of Year 10, more students left school at this point: for the 1977 to 1991 period, this effect was most noticeable for New South Wales (NSW) and Western Australia (WA). In addition, school completion rates were higher where final Year 12 assessments were either entirely school-based (as in Queensland) or included substantial school-based components. Completion rates were also higher in states where substantial numbers of students were able to gain a Year 12 certificate without having their course of study dictated by tertiary entrance requirements.
Curriculum policies are not the only influence on state differences in enrolments in post-compulsory education. Non-policy influences, linked to populations and labour markets, are also important. For example, social differences in access to upper secondary schooling have been well documented (e.g. Dwyer, 1996; Lamb, 1994, 1998; Long, Carpenter, & Hayden, 1999; Williams, 1987; Williams, Long, Carpenter, & Hayden, 1993). Population differences between states (based on socioeconomic status (SES), ethnicity, rurality, indigenity, and private schooling) are likely to influence participation in post-compulsory education and training. Employment, industry structure and labour force differences may also drive participation. Higher rates of full-time employment for 15-19 year-olds in some states may provide viable alternatives to education and training and influence post-compulsory enrolments.
The effects of non-policy factors on state differences in participation are likely to be large. However several studies have found that, even after taking account of labour force and population factors, state differences in post-compulsory enrolments remain. In her study of patterns of retention for the 1977 to 1991 period, Vickers (1995) found that the effects of curriculum and assessment policies were substantial and robust. Curriculum policies had effects on patterns of retention even after social background and youth labour market variables were taken into account. Thus, as a result of deliberate policy choices, some states appeared to be more effective than others in promoting high levels of retention to Year 12. Similar results were reported by Long, Carpenter, and Hayden (1999) who found that Year 12 completion rates varied across states after adjusting for population differences linked to SES, family wealth, ethnicity, rurality and educational achievement.
The issue is not only whether there are state differences after controlling for population and labour force factors, but whether or not the policies adopted by states impact differentially on various social groups. Vickers (1995) found that, within particular states, policies related to post-compulsory schooling not only had effects on overall levels of participation, but also had different impacts on students from different social backgrounds. A key question is whether or not social differences in post-compulsory enrolments vary across states. There is little other work to date that deals with this issue.
Over the past decade, we have seen increasing differentiation of post-compulsory provision, so that many students are now continuing their studies outside the secondary school system. Some of these studies may be equivalent in value to Year 12 qualifications, so the new structures may to some extent be ameliorating the effects of early school leaving. In NSW, it is possible to complete a Year 12 certificate in TAFE, and McGaw (1996) argued that some of the differences between NSW and other states in terms of attrition at the end of Year 10 would be eliminated if equivalent TAFE enrolments were taken into account. In addition to this, all states now include accredited vocational education and training (VET) subjects in the upper secondary curriculum, and there has been a rapid increase in the numbers of students taking these subjects (Fullarton, 2001; Fullarton & Ainley, 2000; Malley, Robinson, & Ainley, 2001; Polesel, Teese, O'Brien, & Ungers, 1998). Many of these accredited subjects lead directly into TAFE courses and ultimately to Certificate Level III or Level IV qualifications. These pathways offer advantages such as progression to university study either directly or through TAFE credits, the award of recognised degrees and diplomas, and access to stable and well-paid employment. Other post-compulsory pathways--such as apprenticeships--may provide employment security, but do not easily function as bridges to further education opportunities. At the lower end of this continuum, some traineeships, short-cycle TAFE courses, and other combinations of training and part-time work represent opportunities that are less attractive in both educational and economic terms.
This paper begins with a global perspective on post-compulsory education and training, and examines overall levels of participation in the entire range of courses and programs, regardless of whether these occur in schools, in TAFE, or through combinations of training and work. This analysis is guided by three questions:
1 Do global patterns of participation in post-compulsory education and training vary by state?
2 Do social background differences appear to influence young people's levels of participation in post-compulsory education and training?
3 Do gifts differ from boys in terms of their levels of participation in education and training?
The second section of the paper examines patterns of youth participation in a more traditional way, by focusing more narrowly on participation in school-provided secondary education and in equivalent forms of study in the senior secondary years. We again disaggregate these patterns by asking three questions that are parallel to those above:
4 Do young people's patterns of participation in secondary school education vary by state?
5 Do social background differences appear to influence the extent of young people's participation in secondary school education?
6 Do gifts differ from boys in terms of their participation in secondary school education?
Data and methodology
The analysis is based on data collected in the Y95 cohort of the Longitudinal Surveys of Australian Youth (LSAY). LSAY is a program of longitudinal surveys of young people, managed by the Department of Education, Science and Training (DEST) and the Australian Council for Educational Research (ACER). The program is designed to provide policy-relevant information on young people's education, training, and transition to work. Y95 base-year data were collected in 1995 and follow-up data have been collected annually since then. In the current report, education survival data are provided on 9 738 young people who commenced Year 9 in 1995 and remained in the survey in 1998.
Two major types of variables are used in the study. First, the education survival variables measure the level of educational attainment until the end of the equivalent of Year 12. Most studies on school retention use a binary indicator--ever vs. never finished secondary school. This study uses a more finely grained approach that involves asking not only whether students leave before Year 12, but when they leave school. The Y95 data include information on the month and year young people leave school as well as year level. By using this information, it was possible to measure survival in terms of `half years' and `full years'. Students who remained in school, or in other forms of education and training such as TAFE, through to October of each year were deemed to have completed that year. Students who participated for more than two months but less than nine months of any year were deemed to have completed a half-year. In effect, these are students who left during a particular year without finishing that year. For the four years from Year 9 to Year 12, six levels of attainment or periods of survival were defined: before the end of Year 10, end of Year 10, during Year 11, end of Year 11, during Year 12, end of Year 12.
The nature of the survival is defined by three different variables:
1 a traditional measure of school attainment which measures survival in formal schooling only (excluding participation in post-school study);
2 a measure which includes participation in school as well as school-certificate equivalent studies (e.g. TAFE-based HSC or full-time study towards a Certificate IV or higher in TAFE); and
3 a variable that records global participation in education or training, including school, TAFE, and traineeships and apprenticeships, for each of the six periods.
These three variables are nested. The first variable records whether a student remained in secondary school through each period, from Year 9 to Year 12, and remained to the end of Year 12 in 1998. Survival in the second variable includes as a minimum the school attainment represented in the first variable, but this attainment level also includes study in school-certificate-equivalent education for those who entered such courses after leaving school. The third variable includes the attainment levels of variables one and two as well as participation in any other forms of study and training, including apprenticeships and short-cycle TAFE courses.
The second set of variables relates to background characteristics and includes state, gender, rural or urban place of residence, type of school attended, socioeconomic status (SES) (composite measure derived from parents' education, parents' occupation and wealth), language background, and early school achievement (measured by performance on numeracy and reading comprehension tests undertaken in Year 9). These variables are used to examine patterns of survival for different groups and as controls to help measure the independent effects of state post-compulsory education and training policy on participation.
Labour force variables were not included in the present study. Although the availability of full-time employment and apprenticeships can influence participation, the relationships between labour force status and participation at a broad state level are far from clear. For example, WA has the highest proportion of full-time employment for 15-19 year-olds, the second lowest unemployment rate and an apparent retention rate just under the Australian average (see Table 1). South Australia (SA) has low employment, high unemployment and yet a low retention rate. The relationships between labour force status and retention across states are inconsistent. An examination of labour force factors and participation will be provided in a future paper.
The approach we have adopted is to develop `survival functions' from multi-wave data. Discrete-time survival analysis methods have been used, since these allow us to examine the conditional probabilities that young people either remain in or cease participating in education or training during or at the end of any given period (for detailed outlines of discrete time survival analysis, see Maller & Zhou, 1996; Singer & Willett, 1993).
In the language of the survival analysis literature, the term `survival probability' refers to the proportion of an initial cohort that survives through each of several successive periods; the term `survival function' refers to plots depicting the patterns of survival probabilities over time. Survival probabilities can be estimated for each period and are interpreted as the proportion of students in that year level who continue on to the next year rather than dropping out. `Hazard probabilities' represent the likelihood that a student will drop out during a particular period. Hazard plots display the consecutive hazard probabilities in graphical form. They are easy to read; each point on a plot indicates the likelihood that the sample or group referred to will leave during or at the end of that period. The overall elevation of a hazard plot indicates the overall risk: love elevation indicates low risk, and this means high survival. On each pilot line, the peaks and troughs indicate relative levels of risk: a high peak indicates a very hazardous period for that sub-sample.
Participation in all forms of education and training
The first hazard plot represents global patterns of participation in education and training for the Y95 sample (see Figure 1). In the second plot, we add `state' to this hazard function: this generates a family of hazard functions. If the plots in this family differ either in shape or in elevation, this suggests that the sequential risks of abandoning education and training are not the same in each state. Inspection of Figure 2 indicates that, on the contrary, the five state-specific plots for global participation are quite similar.
[FIGURES 1-2 OMITTED]
Figure 2 represents a model in which state has been added as a predictor variable to the first model. A measure of goodness-of-fit can be used to indicate whether the newly estimated model represents a significant improvement on the initial one. If the addition of state does not improve model fit, this implies that the sequential risks of abandoning post-compulsory education and training are more or less the same in every mainland state. This is in fact the case: the goodness-of-fit test suggests that Model #2 is no better than Model #1.
This is the answer to the first of our six questions. In every mainland state in Australia, the global levels of youth participation in post-compulsory education and training are much the same. A simple frequencies table extracted from the LSAY data indicated that 87 per cent of the 1995 Year 9 sample continued to participate in some form of education, training, or apprenticeship through to the end of 1998 (that is, they either completed Year 12 or did two years of education or training beyond Year 10). In Western Australia, the level of participation is a little weaker (83%), with a relatively high proportion dropping out of education and training 12 months after finishing Year 10. This local variation does not detract from the consistency of the overall picture, which shows that global participation is high across the nation. If one looks at overall levels of participation, the state in which a student resides makes little difference. Most young people are involved in some form of education or training in Year 10 and two years beyond regardless of the state in which they reside.
Our second question is whether social background makes any difference to global levels of participation. It does. Young people whose family backgrounds place them in the highest SES quartile are almost certain to remain in the system and complete a 12th year of education or training. The vast majority of high-SES students stay on beyond Year 10 (see Figure 3). In reality, the risks they face are negligible. This is not the case for those in the lowest SES quartile. For them, the risks of abandoning education and training are quite substantial. In WA, the likelihood that a young person from the lowest SES quartile will leave education and training altogether at the end of Year 11 is one in five; the equivalent estimate for young people in the east coast states is one in ten.
[FIGURE 3 OMITTED]
Our third question is whether gender makes any difference to global levels of participation (see Figure 4). That is, if all forms of education and training are taken together, are gifts more likely than boys to remain in the system? If boys leave, when do they leave? Is the year of greatest risk the same for gifts and for boys? Official statistics tell us that girls are more likely than are boys to finish high school, but what happens when participation in apprenticeships and other forms of training are added?
[FIGURE 4 OMITTED]
Examination of Figure 4 suggests that, within each state, there are very few differences between males and females in terms of their levels and patterns of global participation in education and training. Queensland may be an exception to this generalisation. However a plausible story emerges from this analysis: if all forms of education and training are taken together, gender differences in participation mostly vanish; probably because boys who leave school at the end of Year 10 are much more likely than girls to gain access to apprenticeships.
Participation in school and equivalent education and training
In the second section of this paper we shift our attention from global participation in education and training and look instead at survival through secondary school. Again we ask the same three questions. Do patterns of participation in school vary by state? Do social background differences influence these patterns? Do girls differ from boys in terms of their patterns of survival through the secondary school years?
We find that survival through secondary school is quite sensitive to state-by-state differences in curriculum policy. Inclusion of `state' as an explanatory variable in the first set of hazard models (global participation) did not improve model fit. However inclusion of `state' in the hazard models for school participation makes an enormous difference. As the hazard plots shown in Figure 5 suggest, each mainland state has a distinct survival pattern for secondary school participation.
[FIGURE 5 OMITTED]
For example, in NSW, the likelihood that a student will leave school at the end of Year 10 is 0.1, or one in ten. This is the highest end of Year 10 hazard among the mainland states, and it reflects a unique feature of NSW curriculum policy. NSW is the only state that still requires young people to sit for externally marked tests at the end of Year 10, and still provides a formal School Certificate recording their achievements at this stage. As discussed later in this paper, in low SES schools, the School Certificate still functions both as a hurdle and as a minimum qualification for entry to low-skilled work or to TAFE. Whether they do well or poorly at this stage, many low SES students opt to leave school after attempting the School Certificate. By way of contrast, for many students (especially those from high SES backgrounds) the School Certificate is now a hardly noticeable event on the HSC highway. By flying past this milestone, the risk that these students will leave school is relatively small and declines over time. For those who survive beyond Year 10, the likelihood of leaving during Year 11 is 0.06, at the end of Year 11 it is 0.05, and the risk of leaving during Year 12 is lower still, at 0.04.
The pattern of survival in Victoria contrasts sharply with that of NSW. The likelihood of leaving school at the end of Year 10 is less than 0.04. That is, the chance that a Victorian student will not continue into Year 11 is less than one in twenty. The risk of leaving rises slightly, peaking at 0.065 at the end of Year 11, and then it declines again. In effect, Victoria carries a very substantial proportion of its Year 10 population into Year 11, but is just as successful as NSW in retaining most of those who stay on. The pattern in SA is similar to that in Victoria, with relatively high overall rates of retention to Year 12. Superficial examination of ABS statistics suggests that apparent retention rates in SA have fallen since 1992. However, these data are misleading, partly because the ABS does not count part-time students. It is now possible to complete Years 11 and 12 in SA on a part-time basis, and the proportions of students pursuing this option are so high that a correct estimate of the SA retention rate entails (a) including part-time students in the census and (b) allowing for a four-ear time lag to completion. SA and Victoria share certain similarities in curriculum traditions. For example, both states abolished the external Year 10 Intermediate Certificate over 35 years ago. During the 1980s, both states created alternative programs leading to a Year 12 qualification, and offered these programs to approximately one third of all senior students. A more complete discussion of the culture of curricular reform in these states is provided in Collins and Vickers (1999).
In WA, the pattern of survival through high school is distinctly different from those of the other states: the end of Year 10 hazard is lower than that of NSW but higher than that of Victoria and SA. However WA survivors who stay beyond Year 10 are much more likely than students in other states to leave either during or at the end of Year 11. During the 1980s, WA had a similar participation profile to NSW (Vickers, 1995). In the late 1980s, however, WA removed its external, norm-referenced Junior Certificate, replacing it with school-based assessments at the Year 10 level. This reform appears to have pushed early school leaving back to the end of Year 11. Our analyses suggest that the transition from Year 11 to Year 12 seems to be the new barrier in Western Australia. Local evaluations of alternative work-based curricula in Perth high schools tend to support this finding (Taylor, 2001).
In Queensland the end of Year 10 hazard is more or less the same as that for WA, but after this the risk of leaving school falls very sharply. Throughout Years 11 and 12, the hazards for each period remain low. This hazard profile for the LSAY Y95 sample is consistent with the profiles Vickers (1995) obtained for Queensland in the 1980s. Low hazards and high retention rates characterise the senior secondary years in Queensland, and reflect the fact that internal Year 12 assessments have been a core feature of this state's upper secondary curriculum since the adoption of the Radford system in 1973. Following Radford, Queensland replaced its external Year 12 examinations and introduced a school-based system that used district-level exchanges of student work between schools to develop common assessment standards through professional discussion. This is a system that has consistently been reevaluated and improved upon over the past 30 years. In 1976, Se Scott Report endorsed school-based assessment but advocated a move from traditional norm-based to standards-based assessment. In 1990, Professor Viviani endorsed `the spirit of Radford' once again, but moved forward another step by abolishing the single tertiary entrance score system and offering universities a `profile' of information about each student rather than a single number.
In summary, our results indicate that Queenslanders and Victorians have the highest overall survival rates, followed by SA, then WA, then NSW. Overall survival to the end of Year 12 is lowest in NSW, since it is impossible to compensate for the very substantial levels of school leaving that occur at the end of Year 10. An argument is sometimes advanced that official high school attrition data overstate the `real' losses to the NSW system, since end of Year 10 leavers in that state often continue studying towards the HSC in TAFE. To evaluate this claim, we developed a measure which includes participation in school as well as in Year 12 equivalent studies (e.g. TAFE-based HSC or full-time study towards a Certificate III or better in TAFE). A family of state-specific hazard plots were generated for this broader measure of upper-secondary education participation. Use of this broader measure made some difference for NSW and WA, but no difference for the other states. However the improvements for NSW and WA were very slight. In both NSW and WA, the hazard plots for the broader measure have a slightly lower elevation than the hazard plots for the high-school-only measure, but the shape of the plots is unchanged. Even when TAFE enrolments are included, the end of Year 10 represents a period of high risk in NSW and WA.
The results portrayed in Figure 5 show that there are marked state differences in rates of school survival. Are there also social and gender differences that vary by state? In looking at the patterns for global participation (survival in any form of education and training), there were marginal differences between males and females: patterns of survival were much the same across states for males and females. There were differences between SES groups, though the patterns within SES groups were not markedly different across states. Does the same apply when looking at the patterns of school survival?
Figure 5 shows that there are distinctly different patterns of survival through secondary school across states. An astonishing result emerges if we examine only the data for students in the top SES quartile: for this group, the differences among the states vanish almost entirely (see Figure 6). Regardless of state, the hazard plots are almost flat. Regardless of whether we are looking at the end of Year 10, or the middle of Year 11, or the end of Year 11, the chances that a student will leave school are less than one in twenty; they are quite low, and more or less constant. For the high SES group, state differences in curriculum policy make little difference to chances of survival.
[FIGURE 6 OMITTED]
The patterns that emerge for students in the lowest SES quartile are equally startling and quite different. The hazard plots have high elevations and sharp peaks. These peaks occur in different periods. In NSW, the highest hazard coincides with the end of Year 10, where the likelihood of leaving school is 0.17 (closer to one in five than one in ten). In WA, it coincides with the end of Year 11; at this point, the likelihood of leaving school is almost one in four. For Victoria and Queensland, the plots are relatively flat, and the chances of leaving in any period are consistently between one in ten and one in twenty. This suggests that, for low SES students, the dynamics of early leaving vary a great deal by state. In other words, state differences in curriculum policy have a major impact on the chances of survival for young people from low SES backgrounds. The chances of survival in school for low SES students depend a great deal on the state in which the student lives.
Finally we compare the patterns of survival through secondary school for males and females (see Figure 7). Here again, the differences are quite striking. There are strong gender contrasts within each state, and there are strong differences between the states for each gender group. NSW has its characteristic high hazard at the end of Year 10, but the likelihood that boy will leave at this point is greater (0.14) than it is for girls (0.95). From the end of Year 10 onwards, boys within each state are more likely than gifts in that state to leave school. For example, in Victoria, the likelihood that a gift will leave school is consistently below one in twenty. For boys in the same state, the likelihood of leaving school is consistently above one in twenty; it reaches one in ten at the end of Year 11. Within gender groups, there are strong state-by-state differences. For example, gifts in NSW are four times more likely to leave school at the end of Year 10 than are girls in Victoria or South Australia. Boys in NSW are twice as likely to leave school at the end of Year 10 than are boys in Victoria, and three times more likely to leave when compared with boys in South Australia.
[FIGURE 7 OMITTED]
What these patterns imply is that boys and girls have different patterns of participation, and that these patterns depend not only on their gender but also on the state in which they attend school. By way of contrast with the boys, girls in every state are more likely to remain in high school. If we compare survival through high school with global survival, we see that males in NSW and Queensland are most likely to leave school at the end of Year 10, at which point most of them enter a TAFE course, an apprenticeship, or a traineeship. In Victoria and SA, males are more likely to make this transition at the end of Year 11 than at the end of Year 10. WA is distinguished by its overall low male retention to the end of high school, and by the relatively high risk that its males will leave the education and training system entirely at the end of Year 11.
The results presented in this paper suggest that, in terms of participation in post-compulsory education and training, state policies seem to have little impact at the global level; that is, most young people participate in some form of post-compulsory education and training for at least two years beyond Year 10, irrespective of the state in which they reside. However this pattern does not hold up when examining the narrower base of participation in senior schooling or its equivalents. In looking at participation in school and equivalent education and training, state policies do matter: differences in organisation and policy in post-compulsory education and training across states have a marked impact on participation. In NSW and WA, substantial proportions of young people leave formal schooling at earlier periods than in Victoria, SA or Queensland. In NSW, where the School Certificate is still in place, the major exit point for early leavers is at the end of Year 10. This is particularly true for boys. In WA, the main exit point for early leavers is now the end of Year 11.
Differences in organisational features related to the provision of senior school certificate equivalents alter slightly the survival rates in states where such provisions exist. In Victoria and SA, senior school certificate courses (VCE and SACE) have not been available to school-age students in TAFE. In NSW, WA and Queensland, TAFE-based provision of senior school certificate courses for school-age students provides alternative pathways for students to undertake senior school study. The effect is to improve the rates of survival in those states and weaken state differences. However the effect is relatively small, still leaving NSW and WA with higher hazard rates than other states at earlier year levels.
The effects of state policy on school survival are not uniform across different demographic groups. The results presented in this paper suggest that they hardly matter at all if you are from high SES backgrounds. Such students appear to be insulated against the impacts of curriculum policy and finish high school, irrespective of the state in which they live. Low SES students have no such protection. They are substantially affected by the qualities and dimensions of state curriculum policy and the organisation of post-compulsory education and training. The chances of survival in school or its equivalents for low SES students are much greater in Victoria and Queensland than in NSW or WA. Low SES students are heavily dependent, in terms of their chances of survival in senior schooling, on the post-compulsory education and training policies adopted by states.
In conclusion, the current work has shown that, in terms of participation in school and equivalent education and training, state policies do matter: they make a huge difference to rates of survival. In future work, it will be important to examine in more detail the specific features of state curriculum and education and training policy that affect participation. It will also be important to document the long-term effects of the state differences by looking at labour market and career outcomes. Only then will it be possible to estimate the extent of the contribution that differences in state policies in education and training make to the amelioration or perpetuation of social inequality.
Keywords curriculum policy educational policy postcompulsory educations school holding power socioeconomic influences state programs Table 1 Labour force status of 15-19 year-olds, by state (%) NSW Vic. Qld WA SA Employed full-time 14.2 11.5 15.8 18.5 12.9 Unemployed 8.4 8.5 9.9 7.9 10.7 Apparent retention 67.2 75.9 77.3 71.1 66.7 (Year 12, 1998) Tas. ACT NT Aust. Employed full-time 15.8 10.2 15.1 14.1 Unemployed 11.1 9.1 7.4 8.9 Apparent retention 62.1 91.0 42.9 71.6 (Year 12, 1998) Source: CDATA, 1996
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Professor Margaret Vickers is Director of the Secondary Teacher Education Programs, University of Western Sydney, Bankstown Campus, Locked Bay 1797, Penrith South DC, New South Wales 2750.
Dr Stephen Lamb is a Senior Research Fellow in the Department of Education Policy and Management, The University of Melbourne, Parkville, Victoria 3010.
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|Publication:||Australian Journal of Education|
|Date:||Aug 1, 2002|
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