Bibliographic record
Abstract
Some years ago, we started talking about the ideas that animate this book, and by mid-2019 they began to coalesce.We met weekly, or pretty close to that, at the Lido Bar or Portuguese Club, in Vancouver or Richmond: drinking, reading, writing and catching up without any timeline to stick to.The following spring arrived and disheveled all our thinking.Our first chapter, "Staying Inside," was written pre-pandemic when we were blissfully ignorant of the absurd new set of meanings it would take on.We avoided writing about the virus, side-eying it from a safe distance, but it kept intruding, sneaking past our defenses, infiltrating our conversations.As we wrote, we were also talking with anyone who had time for us as we tried to sort out our ideas.We were extremely fortunate that three people whose work we particularly admire: Jean-Luc Nancy, Leela Gandhi, and Leanne Betosamosake Simpson agreed to be interviewed -the book would have been very different without them.One of the core questions we grappled with as we tried to pull our argument together was: How come so many people who write about friendship and community are such assholes?We got that question asked of us often.
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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.489 | 0.414 |
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".