Bibliographic record
Abstract
To summarise our findings, the Hippocratic Epidemics case reports is an example of a text whose intended audiences, despite the ambiguities and historical uncertainties about the texts’ composition and transmission, were very firmly delimited as professional and medical. Such closure defines this phase of ancient medicine as particularly territorial and “technical”, on the one hand – no literary pretence, nor broader intellectual appeal of the kind shown by Galen is on the horizon of these writers, nor any explicit attempt to win over lay audiences, at least in the Epidemics.77 Also, it tells us something about the epistemology and didactics at work in the Hippocratic handling of patients, which we can summarise as follows: non-theoretical, observation-based and data-centred; self-standing, i.e. not relying on a system of knowledge or a “syllabus” (compare Galen’s frequent recommendation on which of his books one should read first, which are for beginners, what should follow, etc.), but needing to “support itself” by insuring the memorisation of the repertoires of observations, procedures, risks and mistakes; lack of a synthesis of the empirical data, such as a form of diagnosis, or of the “epistemological extension” that might turn the observed case into an “experiment”.78 The Hippocratic use of individual evidence – the patient case – remained in this early stage a communication of pure data. Individual memory, in conclusion, the reception of an individual intellect – a future student, a training doctor – characterises the audience of these texts, motivates and even determines, concretely, their very existence.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".