Bibliographic record
Abstract
Introduction As some of our earlier discussions have indicated, one major variety of reasoning in which we engage is inductive reasoning, whereby a conclusion is drawn on the basis of experience that is in some way incomplete. We decide that something will be a certain way because we, and perhaps others, have found it to be that way in the past. What we are drawing from for our evidence is what we take to be a representative sample of cases of the thing in question. The better the sample, or range and depth of experience, the more justified is the conclusion drawn from it. The most public way in which we see this kind of reasoning is through the reported results of opinion polls. As early as the Port Royal Logic (1662) logicians have identified invalid inductions as a species of fallacy. Inductions based on fewer than all instances, we are told, often lead us into error. But we inevitably have to reason on the basis of fewer than all instances, so the opportunities for error are extensive. The question is how few instances we can accept before the conclusion we draw is unjustified, and the answer will depend on the contexts involved and the types of things we are reasoning about. The fallacy that arises when we conclude too much on too little evidence has come to be called the Hasty Generalization.
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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.048 |
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".