Pyrrhon, Pyrrhus and the Possibility of the Past: A Response to David Henige
Bibliographic record
Abstract
To a significant degree David Henige’s commentary on selected sections of one of my essays on historiography suffers from a lack of clarity, misses the point of my argument or misrepresents it, or is otherwise puzzling. It further displays an unfortunate modern prejudice against faith vis-à-vis ‘knowledge’, and to that extent it is muddled from an epistemological point of view as well as deficient in its understanding of the nature of religion and of the religious worldview. To the extent that it does with clarity and conviction engage with my argument, it is unconvincing in its objections to it, not least because its philosophical foundation in Scepticism is unstable. Indeed, Henige’s characterization of himself as a ‘Pyrrhonian’, after the Greek philosopher Pyrrhon of Elis (the father of Scepticism), is misleading. Like other historians who claim to employ methodological scepticism, Henige more resembles ancient Pyrrhus than ancient Pyrrhon; for the sceptic’s argumentative ‘victories’ are too costly to be worth the winning (Pyrrhic victories indeed), and the arguments that produce them ‘prove’ too much to be taken seriously. As Berkeley once put it, they at one and the same time ‘admit of no answer and produce no conviction’.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".