"Old-Fashioned and Forward-Looking": Neo-Liberalism and Nostalgia in the Daring Books for Girls
Bibliographic record
Abstract
In this paper, we examine The Daring Book for Girls and its follow-ups for their constructions of gender. We suggest that, as examples of how neo-liberalism finds its way into notions of proper girlhood, the books provide intentional and unintentional “lessons” to their readers in how to perform themselves in these times. In both the American and the Australian edition of the initial text, girls are instructed in complex ways to be the right kind of girl: enterprising with a do-it-yourself (DIY) attitude, tolerant of diversity, and nature-loving. Through the constructions of girls as “good citizens” by rules and strict boundaries, the books present to readers a highly nostalgic version of girlhood while simultaneously serving as a pedagogical manual for neo-liberal times.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".