Bibliographic record
Abstract
This book has argued that philosophy can be done historically. It has tried to explain what is involved in doing so, and it has learned about this enterprise by studying it in action. Now, to conclude, I would like to ask what all of this implies. What can we learn about philosophy from the fact that it may be done historically? How does this fact force us to rethink the nature of the discipline? These are large questions, and I cannot hope to say the last word about them here. But I would like to make some suggestions about how to begin thinking about them, suggestions that are best seen as signposts to further inquiry. The general answer to all of these questions should already be familiar. The fact that philosophy can be done historically shows that the discipline is concerned not just with theories, but with pictures. Philosophers are not just in the business of giving specific, detailed answers to specific, detailed questions. They are also in the business of articulating, developing, and assessing pictures of reality, general conceptions of what the world is like and how we fit into it. This fact is not often recognized. We often describe philosophy with reference to theories and arguments alone, depicting it as a search for answers to perennial questions. But philosophy also strives to offer synoptic visions, comprehensive understandings of ourselves and our situation. Some of these visions prove better than others.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.151 | 0.030 |
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".