Girl Culture and Digital Technology in the Age of AIDS
Bibliographic record
Abstract
I n this chapter we focus on girls’ magazine reading, examining both traditional hard copy and digital versions of popular magazines targeting girls. We investigate how magazines represent issues around female bodies and sexuality and how girls, through the process of reading traditional and digital texts, participate in the information culture that surrounds them. As Angela McRobbie (1999) observes in her essay on girls’ magazines: “For over twenty years feminists have singled out girls’ magazines and women’s magazines as commercial sites of intensified femininity and hence rich fields of analysis and critique” (p. 46). Research on magazines aimed at the young teen market, like the work done on women’s magazines more generally, has been extensive. Investigations into this subject range from McRobbie’s work in the 1980s and early 1990s, where she offered a semiotic reading on the codes of romance found in these magazines, to Dawn Currie’s straightforward content analysis of these texts and their readers. Currie’s work (1999) and additional research demonstrate that readers of teen magazines are mainly young teens and preteens, no matter the inscribed textual age of the magazine (such as Seventeen ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".