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
Clark, Sengupta, Brady, Martinez-Garza, and Killingsworth (2015) and Sengupta and Clark (submitted) propose disciplinarily-integrated games as a generalizable template for supporting students in interpreting, manipulating, and translating across phenomenological and formal representations in support of a Science as Practice perspective (Pickering, 1995; Lehrer & Schauble, 2006). To explore the generalizability of disciplinarily-integrated games, this chapter proposes other hypothetical examples of disciplinarily-integrated games in physics, biology, chemistry, and the social sciences. We explore disciplinarily-integrated games in three categories, beginning with the category involving the nearest and simplest transfer of the template and extending to the category involving the furthest and most complex transfer: (1) time-series analyses with Cartesian formal representations, (2) constraint-system analyses with Cartesian formal representations, and (3) other model types and non-Cartesian formal representations. We close with the discussion of the implications of this generalizability.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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