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 I argue for a semantic distinction between two classes of attitude complements. One class is best modeled in terms of possible worlds compatible with what the attitude holder believes/says, in the tradition of Hintikka. The other is best modeled in terms of centered worlds representing the de se perspective of the attitude holder, in the tradition of Chierchia (in turn inspired by Lewis). Much work has assumed that all attitude complements are to be treated semantically in this second manner. I refer to this hypothesis as Uniformity. Uniformity predicts that all attitude complements should be equally semantically able to host elements that must refer de se, such as shifted first person indexicals or relative tenses. Drawing on new evidence from Nez Perce, I demonstrate that this prediction is false, and argue that the best explanation for the distribution of dedicated de se elements comes from variation in whether attitude complements denote sets of centered tuples or rather sets of possible worlds.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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