Girls and activism in a neoliberal time: How teen girls from Toronto negotiate care, activism, and extraordinary girlhood
Bibliographic record
Abstract
Recently, a number of prominent teen girl activist leaders have been gaining the world’s attention, but how do girls not in the public eye and with less social power think about activism? Moreover, how do girls who may not exclusively define themselves as activists, negotiate their own desire to contribute to social change with challenges they identify as holding them back from doing so? Through qualitative research with eight teenage girls in Toronto, I explore the ways these teen girls define the “activist,” their role in activism, and the challenges holding them back from being more active. My methodology is congruent, reflecting my feminist and youth studies commitment to girls leading research, and my findings indicate that such an approach is crucial in order to truly understand how girls with less social power and public visibility experience the world and their roles within it. Doing so also challenge pre-conceived notions and standards of extraordinary girlhood. The findings coincide with what Catherine Rottenberg refers to as neoliberal feminism. The extraordinariness implicit in visible activism framed the girls from my study’s views on what it would take to be a true activist themselves, which was both intimidating and also at times is in contention with their monumental care and concern for loved ones.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".