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
I investigate the issue of the context-dependence of counterfactual conditionals and how the context constrains similarity in selecting the right set of worlds necessary to arrive at the correct truth-conditions. I propose that similarity is constrained by what I call Consistency and Non-Triviality. Assuming a model of the discourse along the lines proposed by Roberts (2012) and Büring (2003), according to which conversational moves are answers to often implicit questions under discussion, the idea behind Non-Triviality is that a counterfactual statement answers a conditional question under discussion and, therefore, is required to make a non-trivial assertion. I show that non-accidental generalizations which have often been taken to play an important role in the interpretation of counterfactuals, are crucial in selecting which conditional question is under discussion, and I propose a formal mechanism to identify the relevant question under discussion. BibTeX info
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 | 0.000 |
| 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 | 0.001 |
| 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