Bibliographic record
Abstract
The publishing of the articles in this issue of Girlhood Studies coincides with the global events related to the First International Day of the Girl—11 October 2012. Th is is a day formally declared by the United Nations as the one set aside to articulate the challenges girls face and to promote girls’ empowerment and the fulfillment of their human rights. The actual process of gaining official recognition through the United Nations for a specific day is no small feat. The efforts of organizations such as Plan International and even government bodies such as the Status of Women in Canada were key in making this happen in order to address the need for greater understanding of girl-specific issues. In the global context, for example, girls are three times more likely to be malnourished than boys. Of the world’s 130 million out-of-school youth, 70 percent are girls. In the Canadian context, as the Minister responsible for the Status of Women highlighted in an International Day of the Girl message, young women from the ages of fifteen to nineteen years experience nearly ten times the rate of date violence as do young men. Close to 70 percent of victims of internet intimidation are women or young girls, and girls and young women are nearly twice as likely as young men and boys to suffer certain mental health problems such as depression, and anxiety about body image and self-esteem remains prevalent among girls. Th us, while October 11 is a time for celebration, it is also a time for reflection and a reminder about how much work there is still to do.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".