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
Many important decisions depend on unknown states of the world. Society is increasingly relying on statistical predictive models to make decisions in these cases. While predictive models are useful, previous research has documented that (a) individual decision makers distrust models and (b) people’s predictions are often worse than those of models. These findings indicate a lack of awareness of how to evaluate predictions generally. This includes concepts like the loss function used to aggregate errors or whether error is training error or generalisation error. To address this gap, we present three studies testing how lay people visually evaluate the predictive accuracy of models. We found that (a) participant judgements of prediction errors were more similar to absolute error than squared error (Study 1), (b) we did not detect a difference in participant reactions to training error versus generalisation error (Study 2), and (c) participants rated complex models as more accurate when comparing two models, but rated simple models as more accurate when shown single models in isolation (Study 3). When communicating about models, researchers should be aware that the public’s visual evaluation of models may disagree with their method of measuring errors and that many may fail to recognise overfitting.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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