Bibliographic record
Abstract
The dying emperor in Kafka's tale has sent you a message of great importance, entrusted to his strongest herald. But the throne room, the palace, the city are so huge and so crowded with people that the message can never reach you. Reverse the tale and you have the problem of Knowledge Transfer (KT)--brutally illustrated by the fate of New Orleans. Important information, of unquestioned validity, soundly based in research and easy to understand, simply does not get through to practice. A recent evaluation of the Ottawa Ankle Rules makes the point all too clearly. Indeed, the KT problem is worse than Kafka's. Economically motivated people and organizations actively distort the messages--and try to trip the herald--while grossly oversimplified frameworks of understanding include no language in which the messages can be expressed. "More research" is not the answer.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".