Bibliographic record
Abstract
As it happens, we are quite well aware of the origin of a group belief. For instance, the history of baseball in the USA is a kind of contemporary myth whose origin, however, is not mysterious. In the US there is a place called the Hall of Fame dedicated to the great figures in baseball history. The spot can be found in Cooperstown, a small American town in the middle of New York state, that is otherwise totally unremarkable. Why was a building put up there to celebrate the sport that is so emblematic of the United States? Simply because the famous baseball is supposed to have been invented there by one Abner Doubleday in 1839. The date is precise but the myth of origins associated with it is no less so. In the early 19th century Doubleday is alleged to have interrupted some children playing marbles behind the shop belonging to the town's tailor. Then he is supposed to have started to teach them the rules of a new, more exciting game which he had just invented (if we adhere to this myth of origins) and which he proposed to call ‘baseball’. So he marked out a small-scale field on the ground: the first game of this typically American sport could now begin.
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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".