Explanation and Justification: Understanding the Functions of Fact-Insensitive Principles
Bibliographic record
Abstract
In recent work, Andrew T. Forcehimes and Robert B. Talisse correctly note that G.A. Cohen’s fact-insensitivity thesis, properly understood, is explanatory. This observation raises an important concern. If fact-insensitive principles are explanatory, then what role can they play in normative deliberations? The purpose of my paper is, in part, to address this question. Following David Miller, I indicate that on a charitable understanding of Cohen’s thesis, an explanatory principle explains a justificatory fact by completing an otherwise logically incomplete inference. As a result, the explanatory role such a principle plays is inseparable from its status as a (not necessarily successful) justificatory reason. With this interpretation in hand, I then proceed to argue that Lea Ypi’s and Robert Jubb’s recent criticisms fail to undermine Cohen’s thesis, and that fact-insensitive principles, once discovered, are especially helpful for purposes of deliberation in circumstances characterized by changing and changeable feasibility constraints.
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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.055 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".