Bibliographic record
Abstract
The emerging area of girlhood studies considers the ways in which girlhood cultures and experiences are recognized and represented. The field brings together a range of scholarly ideologies and methods, including sociology, cultural studies, psychology, medicine, law, social work, history, gender studies, and literary studies. Girlhood studies seeks to go beyond the foundational work of these disciplines to establish new scholarly parameters, theories, and methods for reading girlhood. Becoming Girl: Collective Biography and the Production of Girlhood emerges at a time of burgeoning interest in the figure of the girl across popular cultures and scholarly discourse. The text draws upon and expands methodological approaches for thinking about the ways that girlhood is produced. Led by Marina Gonick (Mount Saint Vincent University, Canada) and Susanne Gannnon (Western Sydney University, Australia), this innovative new volume brings together important scholars in the field to consider a particular methodological approach for researching girlhood: collective biography. Collective biography is a method of knowledge sharing that involves or contains the life narratives of numerous people; it is engaged in memory work and is a means for acknowledging the ways in which research is inevitably personal and affected by memory. If memory is always fluid rather than fixed, changing over time, accessed, recontextualized, and refashioned, then in Becoming Girl, memory is not an unreliable technology or methodology but rather a significant feminist method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".