Bibliographic record
Abstract
Abstract This paper explains how an assertion may be understood despite there being nothing said or meant by the assertion. That such understanding is possible is revealed by cases of the so-called “felicitous underspecification” of demonstratives: cases where there is understanding of an assertion containing a demonstrative despite the interlocutors not settling on one or another object as the one the speaker is talking about (King 2014a, 2017, 2021). I begin by showing how Stalnaker’s ([1978] 1999) well-known pragmatic principles adequately permit and constrain the felicitous underspecification of demonstratives. I then establish a connection between the satisfaction of Stalnaker’s principles and understanding, and show how that connection sheds further light on the relevant cases. After developing and motivating my proposal, I address some objections to it, then briefly discuss the felicitous underspecification of expressions other than demonstratives alongside contrasting my proposal with a similar one from Bowker (2015, 2019) that concerns definite descriptions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".