Bibliographic record
Abstract
There could be no more intense and threatening conception for the transmission of disease than a theory of some ubiquitous, potent and inescapable infection whose origins are untraceable and whose contact is unavoidable. Contagion in such a conception would be the unstoppable and universal spreading of death, recognized by the symptoms of the malady and the span of its action; impossible to counter by the isolation of its physical source, the outbreak of epidemics would leave its potential victims with quarantine as their only defence: that is to say, separation from the community of either the patients stricken with the disease or the healthy, fleeing the ‘infected areas’ to the countryside or some more distant destination. Moreover, this conception of fatal contagion would be all the more appalling if the theory of infection relied on implicit, imprecise and unperceivable principles, such as was the case before germs could actually be observed and accounted for. In other words, implicit theories belong to the imaginary representations of the world, and the place of man in it; before microorganisms were first seen and understood, the descriptions and prevention of epidemics pertained to evasive systems of thought. Medicine, folklore, literature or religion may then be analysed, not as such, but as discourses where a collective conception is at work. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".