The Struggle of Female Italianists for Recognition in Toronto Post-Secondary Institutions
Bibliographic record
Abstract
One hundred and ten years ago there appeared in the magazine Punch a rather wittily sarcastic poem which foresaw the future ruin of women who wished to pursue learning: O pendants of these later days, who go on undiscerning To overload a woman's brain and cram our girls with learning Youll make a woman half a man, the souls of parents vexing, To find that all the gentle sex this process is unsexing.Leave one or two nice girls before the sex your system smothers, Or what on earth will poor men do for sweethearts, wives and mothers?The poem confirms that "nice girls" do not go on to pursue higher education; it makes them unfit wives and mothers.It implies that, for the same reason, "nice girls" do not strive to become members of that group that imparts learning.This paper will deal precisely with the latter category, the women who have heard the calling to academia, and who have answered it, at times struggling against what must have seemed to be insurmountable obstacles, not the least of which was the socially felt, usually male generated, stigma of not being a "nice girl", of not being fit to be "sweethearts, wives and mothers."I shall focus primarily on the entry of women faculty into the Department of Italian Studies at the University of Toronto and into the Italian Section of the Department of Languages, Literature and Linguistics at York University.The issues addressed, however, are not limited to female faculty of just one or two institutions.As is made profoundly clear in a recent study by Paula Caplan, entitled Lifting a Ton of Feathers, the women of
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.062 | 0.021 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".