Refining the Classification of Schemes
Bibliographic record
Abstract
It would be very helpful for users of the schemes to have a more refined system of classification, so that the user could search through to find a scheme applicable to her needs in a given case by searching under other, more general ones where the particular scheme being sought is known to fit. It is already fairly evident from the compendium of schemes that some schemes fit under others as subspecies of them. For example, one of the most common schemes is argument from consequences. It is closely related to practical reasoning. Other schemes, like those for the slippery slope argument, often fit under the category of argument from consequences. However, such classifications are not as straightforward as they initially seem. For example, some slippery slope arguments fit under the category of arguments from precedent, and therefore may not fit the scheme of argument from consequences, at least in any straightforward way. Another very common scheme under which many others fit as subspecies is the scheme for argument from commitment. Here we have a cluster of schemes that are closely related to each other, but in complex ways. Schemes that are very general, like those for argument from consequences and argument from ignorance, are related to many other, more specific schemes that fall under them. This chapter sets us on the road to beginning the research project of taking such clusters of schemes and investigating how they fit together with their neighboring schemes.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".