Bibliographic record
Abstract
ABSTRACT: The common ground in an inquiry consists of what the participants agree on, at least for the sake of the inquiry. The relations between the factual and linguistic components of common ground are notoriously difficult to trace. I clarify them by exploring how modal disagreements – disagreements about how things might be – interact with the linguistic and the factual common ground. I argue that modal agreement is essential to common ground of any kind. KEY WORDS: common ground, possible worlds, semantics I will begin my discussion of common ground and modal disagreement with an illustration of the complex interactions between beliefs about meaning and beliefs about the facts. Andy, Bob and Charles are discussing American politics, when Andy says, ‘Cheney is a vet. He served in Vietnam. ’ Bob and Charles dispute this, both saying ‘What? Cheney is not a vet. ’ But while Andy and Bob mean veteran by ‘vet’, Charles means veterinarian. We know how to describe these disagreements, and even what it takes to resolve them. Andy and Bob agree on the meaning of ‘Cheney is a vet’, but disagree about the facts while Andy and Charles disagree about the meaning of that sentence, but may otherwise agree on the facts. But now along comes Donald who says
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".