Replying to Armour:Certainty and Exceptionalism: Threats to a World-Humanities?
Bibliographic record
Abstract
This article explores attitudes underscoring arguments I believe are located in Professor Armour's address in the present special issue. I first show how Armour's arguments are intertwined with a resolute belief in the existence of a unique form of knowledge, one particularly attuned to the work of humanities scholars, and then go on to argue this certainty is linked to an antecedent attitude, one of exceptionalism. Spelling out what I find to be troubling about this species of argument leads into thoughts around how a world-humanities might unfold. Such a field must develop as a conscious attempt on the part of scholars to bring about dialogue around how humans can find an appropriate place in the natural world.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.022 | 0.029 |
| 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".