The problem-ladenness of theory
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
The cognitive sciences are facing questions of how to select from competing theories or develop those that suit their current needs. However, traditional accounts of theoretical virtues have not yet proven informative to theory development in these fields. We advance a pragmatic account by which theoretical virtues are heuristics we use to estimate a theory’s contribution to a field’s body of knowledge, and the degree to which it increases that knowledge’s ability to solve problems in the field’s domain, or problem-space. From this perspective, properties that are traditionally considered epistemic virtues, such as a theory’s fit to data or internal coherence, can be couched in terms of problem-space coverage, and additional virtues come to light that reflect a theory’s alignment with problem-having agents and context in a societally-embedded scientific system. This approach helps us understand why the needs of different fields result in different kinds of theories, and allows us to formulate the challenges facing cognitive science in terms that we hope will facilitate their resolution through further theoretical development.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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