Fighting the gap: Does military service reduce the gender wage gap
Bibliographic record
Abstract
Research on the gender wage gap consistently demonstrates that women continue to earn less than men do despite equity laws, policies aimed at reducing the gap and women now surpassing men in educational attainment. This wage gap has continued to stagnate for the last twenty years. Drawing on data from the 2002 Canadian Community Health Survey (CCHS) Cycle 1.2 and the Canadian Forces Component of the CCHS, this thesis uses ANOVA and OLS regressions to examine the effect of human capital and family characteristics on the gender wage gap of civilians, reserves, and regular force members. Results show the gender wage gap is highest among civilians, smaller in the reserve force, and smallest in the regular force. There is little support that human capital alone explains these wage gaps. Moreover, contrary to previous research, females in the military do not suffer a wage penalty for marriage or children. These findings support the idea that military service plays a role in reducing the gender wage gap for women; however, more sensitive measures are needed to further examine these results.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.010 | 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".