Former enemies meet again overseas
Bibliographic record
Abstract
My dad was a child in London during the war. In the late 1950s he emigrated to Canada to work as a geologist in a gold mine in Northern Ontario. As miners do, they tend to drink and tell stories and my dad overheard one exchange between two very burly old drillers that went something like this: Franz: So Frank - you were in the war? Frank: Yeah, Italy. Franz: Ach so was I! Frank: I got pretty banged up there. Franz: I also! What happened? Frank: Well, we were driving into a little village, we turned the corner and in front of us was a German AT gun. I shot up the crew with the bow MG, but they managed to get a round off and blew our tank all to hell... You? Franz: I was manning an anti-tank gun in a little Italian village when a Sherman came around the corner and machine-gunned us badly, but we got a round off and took them out....Dead silence descends on the bar.Frank: Buy you a drink? Franz: Sure, the next one's on me.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.650 | 0.364 |
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".