Theories, queries, “frames” and language games: Commentary on Wall, Crookes, Johnson Weber (2020) (and the literature on risky-choice framing)
Bibliographic record
Abstract
In a recent article, Wall, Crookes, Johnson and Weber (2020) claim that Query Theory has better explanatory success in accounting for recent data than the Explicated Valence Account of Tombu and Mandel (2015). In this commentary, I first argue that this claim is not supported by the full range of available evidence. I then draw attention to the pernicious problem in framing studies in which researchers do not adequately ensure that framing manipulations are what they claim to be—namely, extensionally equivalent re-descriptions of the same events or event classes. The difficulty of estab- lishing extensional equivalence in the context of experimental language games (such as the Asian Disease Problem) is under-appreciated. Unfortunately, inter-subjective agreement that the extensional equivalence assumption is met, even amongst a majority of respectable decision theorists, does not constitute sufficient evidence that it is met. Empirical evidence challenges the equivalence assumption, raising meta-theoretical questions about the integrity of some framing research.
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.
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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.029 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".