Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.At Witch, Please Productions—the queer feminist media production company I co-founded with Marcelle Kosman and Hannah Rehak—we have a motto that underpins and guides our collaborative ethos: “no one is in trouble.” Our insistence that no one is in trouble is rooted in our queer feminist ethics of care, one that prioritizes the wellbeing of our collaborative team and by extension our larger community of collaborators, interlocutors, and listeners. While stated overtly and frequently at Witch, Please Productions, this care-based ethos of collaborative media creation emerged gradually for me through various collaborative projects, including The SpokenWeb Podcast and the Amplify Podcast Network, both projects that were also, notably, built through queer feminist collaborations. By prioritizing care and wellbeing from the beginning, and building projects from the ground up with that ethos in place, we are collectively learning new ways to make things together. This article takes the form of a conversation with some of my key collaborators, modeling the playful collectivity of these projects, to match in form what I am articulating in content: that we create more radical, expansive, collaborative scholarship when we centre care, relationships, and the wellbeing of the collective.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.868 | 0.804 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".