Over-qualification in the Workforce: Do Indigenous Women and Men Benefit Equally from High Levels of Education?
Bibliographic record
Abstract
Using data from the 2016 Census, this study examined the level of education–job mismatch (over-qualification, in particular) in the Canadian labour market among Indigenous women workers aged 25 to 64 who received post-secondary education. Their rate of over-qualification was compared with that of Indigenous men as well as non-Indigenous workers. In doing so, this study aimed to shed some light on the effect of post-secondary education on labour market outcomes by investigating whether Indigenous men and women benefit equally from their post-secondary education. Compared to their non-Indigenous counterparts and Indigenous men, Indigenous women workers with university-level education (bachelor’s degree or higher) were less likely to be over-qualified. Conversely, Indigenous women workers with post-secondary education lower than university level were more likely than non-Indigenous women and Indigenous men to be over-qualified. This pattern persisted after sociodemographic factors were controlled for. The results suggest that, among those with a post-secondary education, higher levels of education were especially advantageous to Indigenous women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".