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
The main goal of semantic fieldwork is to accurately capture the contribution of natural language expressions to truth conditions and to pragmatic felicity conditions, by interacting with native speakers of the language under investigation. Most semantic fieldwork tasks (including, for example, acceptability judgment tasks, elicited production tasks, and translation tasks) require the researcher to present a discourse context to the consultant. The important questions then become how to present that context to consultants and how to best ensure that the consultant and the researcher have the same context in mind. We argue that phenomena which rely on controlling for interlocutor beliefs are particularly well suited for the storyboard elicitation methodology. This includes “out-of-the-blue” scenarios, which we treat as a special type of discourse context that must also be controlled for. We illustrate these claims by presenting novel storyboards targeting the de re/ de dicto ambiguity and verum marking.
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 | 0.002 |
| 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 | 0.001 | 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