** * Forthcoming MESA Working Paper *** The Effects of Family Income, Parental Education and Other Background Factors on Access to Post-Secondary Education in Canada: Evidence from the YITS
Bibliographic record
Abstract
This paper exploits the unprecedented rich information available in the Canadian Youth in Transition Survey, Sample A (YITS-A) to investigate issues related to access to post-secondary education (PSE). The questions we ask are basically two-fold: i) What are the various influences on access to PSE of an individual’s background, including more traditional measures such as family income and parental education, as well as a broader set of measures such as high school grades, social/academic “engagement, ” and other cognitive and behavioural influences? and ii) How does including such a more extensive set of variables than has been possible in previous studies change the estimated effects of the more conventionally measured family/parental influences (family income and parental education) on access to PSE, and thus indicate how much of the latter influences operate through (or otherwise proxy) the effects of the broader set of variables, thereby isolating the direct – as opposed to indirect – influence of these traditional measures on access? Utilizing multinomial logit models to capture the choice of level of PSE (i.e., college versus university) we find that parental income is positively related to university attendance, while having only a minor effect on college, but this effect is greatly diminished once parental education is included in the estimation. Similarly, the importance of parental education to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".