Bibliographic record
Abstract
In this paper I challenge the common wisdom (see Dummett and Davidson) that sentences are the minimal units with which one can perform a speech act or make a move in the language game. I thus sit with Perry and Stainton in arguing that subsentences can be used to perform full‐fledged speech acts. In my discussion I assume the traditional framework which distinguishes between the proposition expressed and the thought or mental state (possibly a sentence in Mentalese) one comes to grasp when using/understanding an utterance (or sentence‐in‐a‐context) expressing a proposition. Unlike Stainton, I will argue that the proposition expressed by a subsentential assertion and its corresponding thought are not the end product of a pragmatic process of free enrichment. I shall defend the view that a thought may concern something without the thinker having to represent that very thing. This should help us to resist the view that with the utterance of a subsentence enrichment is mandatory. I will further argue that subsentences and their corresponding thoughts are situated. Because of that we can successfully interact and engage in joint ventures using subsentences and be guided by thoughts without having to enrich them. The fact that the actors’ unenriched thoughts are co‐situated may suffice to explain the positive outcome of their joint project. Last but not least, I will also show how the picture I propose gains further support by taking on board Perry’s distinction between reflexive truth conditions and incremental truth conditions (or official content). Since competent speakers can grasp an utterance’s reflexive truth conditions without having to grasp its official content (roughly, the proposition expressed) they can successfully interact without their thoughts having to undergo a process of free enrichment. Moreover, if I’m right in arguing that an utterance’s reflexive truth conditions are the best tool to classify the semantic features of one’s mental state (or sentence in Mentalese), we can further explain mental causation and linguistic communication without appealing to free enrichment.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".