Minimal models, feminist epistemology, and diversity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it