Bibliographic record
Abstract
Sitting in my study, writing about human aggression, man’s inhumanity to man, I am bemused by the spectacle of eagles, ducks, fish, and seals involved in the unceasing cycle of life, violence, and death unfolding outside my window—a delicate symbiotic balance struck between predator and prey. Why this is so might be illustrated by the observations of my twenty-one-year-old son, Alexei, who last winter was thrilled to get a contract to paint the Chinese crimson and gold banners bedecking a festival stage and hand out leaflets in Vancouver’s notorious downtown eastside, the violence and drug center of Canada. When I ask what it was like to hang around that unprepossessing part of town, Alexei says it was strangely exhilarating: “People are hunting and gathering, eating and sleeping, and fighting and socializing right in front of you. It’s so real! It’s not the suburbs. It is like, like…” “The wilderness?” I offer. “That’s it!” he exclaims. “It’s like the wilderness; it’s so real because there is nothing filtered or refined, nothing insulating you from reality. One minute you are safe, nothing is happening, and the next minute you could be in danger.”
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.061 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".