Bibliographic record
Abstract
Some years ago I wrote a book about millennial anxiety that concluded with an affectionate description of the Canadian city, Winnipeg, where I spent seven years of my life before heading off to college. Recalling the annual waves of floods, blizzards, tent-caterpillar infestations, and summer hailstorms that battered trees and cars into failure, I labeled the town known officially as “The Gateway to the West” as “Plague City.” It is indeed a bleak and forbidding place, where the sidewalks crack from deep winter cold snaps and summer seems to last about a month. Not surprisingly, its longtime residents are resilient, friendly, and cooperative. They look out for each other. The rest of us take the first viable opportunity to move somewhere less hostile to human existence. The year 2020 has seen most of the world’s cities transformed with startling swiftness into new versions of a genuine Plague City, not just a Midwestern town beset by routine environmental events. The novel coronavirus outbreak that likely started in wet markets in China spread with deadly speed and virulence across the planet, creating global pandemic conditions unprecedented in human history, even including the 1918 Spanish Flu, the 1665 Great Plague, or the Black Death. The final death tolls may prove smaller than those world-historical crises—one hopes substantially smaller—but the overall social and economic effects are larger, and will continue longer, than anything the world has witnessed before.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.153 | 0.068 |
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".