Bibliographic record
Abstract
This commentary was largely written on unceded Lekwungen territory, now known as Victoria, BC, and was delivered at the Pacific APA on traditional Musqueam, Squamish and Tseil-Watuth land, known as Vancouver, BC. The material conditions that enabled me to write my commentary on this excellent and insightful book were made possible by the dispossession and genocide of Indigenous people – colonial practices that exemplified the phenomenon of dehumanization that is the subject of Making Monsters. It is largely because Indigenous people were not seen as genuine people by those who colonized their land that we are doing this work under these circumstances; this is just one among many reasons why dehumanization is not an abstract philosophical topic, but something that has shaped the circumstances of many of our lives. My comments on Smith’s book have less to do with the internal coherence of the conceptual framework as a whole, and much more to do with the ways that conceptual framework can help us counteract the oppression related to dehumanization. To that end, I want to follow two primary threads. The first thread involves Smith’s emphasis on the psychological dimension of dehumanization and the places where that focus might result in strategic difficulties. The second thread pushes in the opposite directions from some of Smith’s other critics by pointing out some places where criticizing a practice as contributing to dehumanization might not go far enough.
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 | 0.008 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.224 | 0.124 |
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".