Bibliographic record
Abstract
The anthology you are holding was first inspired by the events leading up to and surrounding the sexual assault trial of Canadian musician and broadcaster Jian Ghomeshi in early 2016.The Ghomeshi case seemed to instigate a breach in what had been a collective and public silence.Women began talking-out loud, on social media, and to each otherin a way I hadn't heard before.Eventually, this coalesced as a movement on social media under the hashtag MeToo, as more and more women came forward with accusations of sexual assault by high-profile men in Hollywood, in the media, and across many professions.In my community, too, women were talking.I organized a gathering for whoever needed the comfort of company.Some in attendance spoke of their own experiences of sexual assault.Others chose to listen.The air in the room thrummed.The trauma, violence, and wounds of those experiences were exported from silence and individual bodies into a space that held the pain collectively.The relief of hefting that weight off of ourselves was palpable.And this hefting was no small thing.Around this time, I was approached about editing an anthology of poems that would serve as an extension to that kind of space-a space, here on the page, that would invite readers into this relief from silence.Each poet who answered the call to be part of this anthology is contributing to the change that the #MeToo movement continues to inspire.Unfortunately, there were unexpected challenges when it came time to publish the collection.At each juncture, I contacted
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.721 | 0.703 |
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".