Bibliographic record
Abstract
The famous gulf between the arts and the sciences comes from the current pervasiveness of scientific illiteracy.The resultant increased fragmentation of science threatens scientific research; the resultant increase of the portion of the population of the advanced world that shows general ignorance of science threatens Western culture and democracy, and thus science itself.Historians and popularizers of science can help reduce this gulf.Introducing science historically can help solve many acute social and political problems.Historians of science can try to bring this about.Discussions of the social role of intellectuals (and of historians of science in particular) require a great deal of a sense of proportion, since most people are still barely educated, hardly familiar with science, much less with its history.Science nonetheless influences lives.Current increased inaccessibility of science boosts the prevailing excessive scientific specialization that scientific and technological progress have imposed.Consequently, the search diminishes severely for comprehensive overviews of the world and even of specific parts of it, leading to disorientation that imperils even the normal functioning of science.There is then a vital need for a search for remedy.Philosophers and historians and popularizers of science can help develop the tools necessary for this vital search.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".