Bibliographic record
Abstract
The word ‘contagion’ contains a buried metaphor pertaining to ‘touch’. But the notion has been generalized to express all manner of pathogenic transmission through proximity, and then generalized again to express moral contamination, imitative emotions or the psychology of crowds. Through such analogical applications, the history of contagion becomes even more extensive, one that relates not only to the best scientific and philosophical explanations from the ancients to the early moderns concerning the spread of diseases, but, by extension, to an analysis of the psychodynamics of groups. Given that microbiology belongs only to the last two centuries, earlier thinkers were challenged to account for contagion according to their ‘received’ philosophies of nature, or in terms of what they presumed to see and verify prior to an understanding of microorganisms. Consequently, they had no choice but to turn to the language of correspondences, occult and spiritual forces, environments and temperaments, poisons, vapours, stares and the polluting touch. But when these operations were applied to the transfer of passions and ideas it was no longer for a lack of understanding of the microbiological world, but of the emotional and cognitive mechanisms whereby minds copy passions and belief structures in seemingly spontaneous, subconscious and often destructive ways. These are socio-psychological phenomena merely resembling pathogenic operations; the relationship would appear to be one of pure metaphor. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".