Gender and environmental education in the time of #MeToo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
[Extract] The second special issue of The Journal of Environmental Education devoted to gender and environmental education comes at an interesting moment in popular culture. The #MeToo movement, founded by activist Tarana Burke in 2006 to support girls and women of color who had experienced sexual violence, became a global phenomenon in 2017 when celebrities popularized the phrase as a hashtag when discussing sexual violence and harassment in the entertainment industry. The hashtag took off, with 12 million tweets in the first 24 hours (Mendes, Ringrose, & Keller, 2018), from 85 different countries that had at least 1,000 tweets each (Park, 2017, para. 1). While the movement has been galvanizing for many (Mendes et al., 2018), feminist scholars nonetheless have concerns about its unfolding, including the erasure of Black activist women in many accounts (Emejulu,2018) and lack of awareness of how race, class, and celebrity status are factors that influence why certain testimonies are more likely to be heard and believed (Zarkov & Davis, 2018). Zarkov and Davis (2018) also worry that too much focus on individuals may downplay that sexual violence is a collective problem that requires "grass-roots activism as well as transforming institutions" (p. 5). At this point, as Mendes et al. (2018) observe, "we still know very little about what hashtags like #MeToo actually do; or whether and how they can produce social change" (p. 3).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it