Bibliographic record
Abstract
There are good reasons to think that there was a body of truths generally accepted by the scientific community under Abbasid rule during the middle ages. However, the indicators initially established by the scientonomy community to guide us in reconstructing past mosaics are not applicable in the case of the medieval Arabic scientific mosaic. Instead, by attending to the particular way that knowledge was disseminated in this community, we can see the primacy of the concepts passed down in authoritative texts. It is proposed here that a good way of determining which texts, and therefore theories, were accepted would be by tracking the unique record of licenses to teach [ʾijāzāt] particular texts that exist from this period.Suggested Modifications[Sciento-2017-0003]: Accept the following propositions concerning MASM in c. 750-1258 CE in the Abbasid caliphate:Authoritative texts are indicative of theories accepted in MASM.Licenses to teach [ʾijāzāt] are reliable indicators of which texts were considered authoritative in MASM.Accept that the following method ought to be employed when reconstructing the theories accepted in MASM:Teaching License Method: A proposition can be said to be accepted in MASM if the evidence of the licenses to teach [ʾijāzāt] indicates so.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".