Bibliographic record
Abstract
Often associated with increasing student achievement and improving the educational experiences for both girls and boys, single-sex schooling has garnered renewed interest among education professionals, researchers, politicians and parents. The purposes of this study were to (a) review recent newspaper articles to determine how the issue of single sex schooling was being defined, and (b) to undertake a systematic review of academic research focussing on single-sex schooling. Single Sex Schooling in the News The way that issues are defined by news media often influences how the public, policy-makers, and practitioners view and understand those issues. In order to understand how the issue of single-sex schooling was being defined, an inventory of newspaper articles published between 2003 and 2004 was created using the LexisNexis and Canadian Newsstand 2databases. Single-sex schooling was defined in five dominant ways: (1) as an educational benefit issue; (2) as a 3 4learning styles issue (girls and boys learn differently, thus require different environments); (3) as a choice issue; (4) as a 5gender gap issue (with one hundred percent of these articles arguing or implying that boys are disadvantaged within the 6current system); and (5) as a distraction issue. A dominant sub-text in these newspaper articles was the potential for single-sex schooling to address and mitigate the 7disadvantage of boys. Single-sex schooling was presented as a means to helping boys improve their concentration,
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".