Bibliographic record
Abstract
This thesis investigates the wide gender gap in education. In Canada in 2000, only 40% of those graduating with undergraduate degrees are male, and 48% of those graduating with Master's degrees are male. And the gap is still widening. Only at the Doctorate level do males outnumber females: 59% to 41%: but this gap has closed from 64% males to 36% females in the two year period from 1998 to 2000. This situation is similar in the United States. This seems to be a massive failure in equity. I interviewed six teachers at the kindergarten, primary and high school levels. The primary level is where the problem begins. Teachers reported that boys learn differently from girls in various ways, and the education system privileges girls; that male teachers are needed as role models; that more activities are necessary for boys; that gender separated classes sometimes work for boys, without jeopardizing female accomplishments; and more. Finally, I offered ten recommendations to reform this inequitable education system.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.022 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| 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".