Bibliographic record
Abstract
This paper draws on feminist epistemology and epistemologies of ignorance to consider debates over ‘minimal’ economic models and to showcase implications for diversity. Minimal models are highly idealized models put forward without specific empirical support. Criteria for evaluation include intuition, fit with background knowledge, and imagination. Minimal models may be interpreted modally, as giving us ‘how possibly’ rather than ‘how actually’ explanations; they are said to add to our ‘menu’ of possible explanations. Feminist epistemology emphasizes that perspectival differences have epistemic consequences; epistemologists of ignorance show how social position influences what we do not know. Using the checkerboard model of segregation as an example, I argue 1) that because evaluation of minimal models rests on subjective criteria, their use gives us reasons to pursue diversity in the epistemic community and 2) that because of ignorance, adding to our menu of possible explanations can have epistemic risks.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".