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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".