Bibliographic record
Abstract
This essay asks whether a pragmatist philosophy of history can make sense of the notion of historical facts. It is tempting to think it cannot, since pragmatists insist, as James puts it, that the trail of the human serpent is over everything. Facts, by contrast, are typically thought of as something untouched by the human serpent, something impervious to what we think and do. I argue, however, that there is a way of understanding facts that is perfectly at home in pragmatist philosophy of history. Drawing on work by Robert Brandom, I propose that facts be interpreted inferentially. On this view, to call something a fact, or to say that the facts make my beliefs true, is simply a shorthand way of saying that a particular sort of relationship exists among certain sentences. I further show that this inferential understanding of facts is fully compatible with the distinctive features of historical inquiry. In particular, it is compatible with history’s irreducibly narrative character, and with the way different narratives can reveal radically different facts. Finally, I use this account of historical facts to respond to a classic criticism of pragmatism: the charge that pragmatism is narcissistic. I argue that pragmatism is narcissistic in only the minimal sense that it cannot countenance theory-neutral givens. But pragmatists can happily grant that there is more to truth than consensus, and that our claims are answerable to facts that everyone can get wrong.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".