Bibliographic record
Abstract
Abstract: In contemporary North American contexts, to say that a claim is oracular is seriously to undermine its philosophical credibility. My thesis is that this negative judgement of oracularity is unwarranted and that it is rooted in an excessively narrow notion of what constitutes ‘good’ philosophy. More specifically, I argue that oracular utterance is appropriate to the expression of views that regard the phenomena towards which they are directed as radically, non‐systematically integrated wholes. Importantly, such views are falsifiable—or at least as falsifiable as scientific paradigms, which, in one important respect, they resemble. However, I argue further that there is good reason to think that such views cannot, without distortion, be expressed using the systematic‐analytic forms of argumentation that are frequently regarded as essential to the pursuit of philosophy. Yet the questions that they compass, concerning the way in which parts are related to the wholes that they constitute, fall squarely within the purview of traditional metaphysics. Thus, in proscribing oracular utterance we divest ourselves of the opportunity to contemplate world orders of potential philosophical interest that systematic‐analytic argument is incapable of conveying to us.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".