Women in formal and informal education. International comparative perspectives in the history of education
Bibliographic record
Abstract
Comparative Perspectives in the History of Education, edited by Maria Lucenti, is a collection of stories and studies emerging from the VIII Congresso della Societá Italiana delle Storiche (SIS) -La storia di genere: percosi, intrecci, prospettive conference organized online in the summer of 2021 in cooperation with the University of Verona.Elisabetta Serafini explains in the foreword that SIS was founded by women's movements at the end of the 1980s, and it discusses women's history and gender history at regular conferences since the first one held in 1995 in Rimini.At the VIII conference, 144 panels were led by 180 people from European and non-European colleges and research institutions.The topics discussed include a critique of gender binarism, lesbian subjectivities, work, migration and mobility movements for citizen's rights and ecofeminism, women's writing, autobiographies and many more.The SIS places significant emphasis on the relationship of gender and education, as well as equal opportunities for both sexes.The purpose of the edition is to raise awareness of the importance of gender studies and equality, especially in formal and informal education, in this case in western Europe, Canada and North Africa, using the "past as a compass for present and future choices", as Elisabetta Serafini formulates in the foreword.Although the situation has become somewhat balanced recently, there are still imperfections that need to be dealt with in Italy.More girls go to school in the world than ever before, still they are less in number than boys (according to 2015 data).Illiteracy is also a serious issue, e.g. from the 780 million illiterate people in the world, 520 million are female, as it is presented in data from 2013 to 2015 (Seager, 2020).It also turns out that girls are more likely to leave school due to economic hardships.The number of female higher education students has increased, however, it must be considered that women were denied the opportunity of studying or receiving a diploma for a very long time before that could happen.This increase in the number of female students is still lower than that of male students, and there are institutions in the world that still reject women.Not only gender but also financial status can be connected to graduating from higher education: children of wealthier families are more likely to actually finish university.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".