Bibliographic record
Abstract
INTRODUCTION Contextualism raises doubts about objectivity. It can also politicize what we take to count as knowledge and slip into what I will call “the politics of making.” Countering contextualism, some philosophers have made radical moves to secure objectivity by turning to either a supersensible realm of “mind-independent objects” or some essential feature of human nature for epistemic ground. In this paper I reject both contextualism and any kind of ahistorical commitment to objectivity. By taking architecture as paradigmatic of making, I defend a view of epistemic warrant that is neither wholly contextualist nor committed to any transhistorical form of objectivity. I maintain that educational practice steers clear of this tension even when theorists in education may not. This is not accidental. Educational practices are discursive and, as such, inferential and normative. Their rationality, and their objectivity, is secured by the logic of inference. I defend this view.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.083 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".