Bibliographic record
Abstract
It’s a privilege to publish a book, and it’s a privilege more for that book to receive time and attention from thoughtful interlocutors. I’m grateful for the care my critics have taken in their treatment of the book, and I hope that this exchange is as illuminating for them – and readers of this symposium – as it has been for me. The contributions span a helpfully wide range of viewpoints. Hrishikesh Joshi presents a sceptical challenge to the basis of my project, questioning whether the concept of microaggression has empirical tenability or social utility. Emily McTernan and I have a more intramural dispute; McTernan also takes microaggression seriously, but she disagrees with me about how it should be conceptualized. Jeanine Weekes Schroer’s view is probably the closest to mine; ours might be called a family dispute, in agreement on the big questions but still not quite identical on what to do with our shared conceptual resources. I’ll proceed from the outside in, from Joshi’s scepticism to McTernan’s internal critique to Schroer’s friendly amendments. I don’t have the space to address all of the many thoughtful points my critics raise, but I hope I’ve drawn out the most interesting places where we disagree.
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 | 0.100 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.019 | 0.053 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.029 | 0.052 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".