Bibliographic record
Abstract
It has been said that the shortest measurable unit of time is the period between the traffic lights changing to green and the sound of the taxi driver blowing his horn when stuck behind your car. To this quantum event can be added the attention span of an orthopaedic surgeon, which despite being as ephemeral as Schrodinger’s cat, may turn out to be an important phenomenon after all. Our modern world gets ever faster and more complex. Each year it seems like Christmas comes every six weeks. This year is more than half over and apart from the Druids celebrating the solstice or research fellows counting how many papers they are likely to get published this year who would know? The information revolution dumps huge amounts of information on our virtual desks to read, analyse, and file. While I sit here pondering this missive, I am reminded of the information revolution in a meaningful way. My emails are full of deadlines from coworkers in Vancouver, Calgary, Montreal, London, Leeds, Pittsburgh, and Sydney. How did we ever get by without the World Wide Web and computers? Visiting my local museum recently (the Museum of Victoria for those of …
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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".