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
Abstract People often predict that they, and others, will be biased by sunk costs—they think that investing in an object or goal increases how much one values or wants it. In this article, we use sunk cost predictions to look at people’s theory of mind and their conceptions of mental life. More specifically, we ask which mental states and motivations are seen as underlying the bias. To investigate this, participants in two preregistered experiments predicted whether different kinds of agents would be biased by sunk costs, and also assessed the agents’ mental abilities. Participants predicted that some kinds of agents (e.g., human adults and children, robots) would show the sunk cost bias and that others would not (e.g., raccoons and human babies). These predictions were strongly related to the participants’ assessments of whether the different kinds of agents are capable of seeing actions as wasteful, but also related to their assessments of the agents’ capacities to feel regret and frustration.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it