The Public Dimension of Meaning
Bibliographic record
Abstract
The philosophical discussion of conceptual content and linguistic meaning in the 20th century has been dominated by two contrasting approaches - the descriptive-internalist approach, and the causal-externalist approach. Recent semantic models, for example the two-dimensional semantics of Jackson and Chalmers, attempt to integrate these two approaches. In this dissertation I explore a series of puzzles that highlight points at which the resources of these two approaches combined fall short. Particularly, the dissertation is an argument for the claim that facts about a linguistic community can affect the conceptual and linguistic content of individual members of that community, developing insights of theorists such as Quine, Wittgenstein, Kripke, Lewis and Davidson. \nThe study proceeds along two lines simultaneously, as an investigation into puzzles concerning conceptual content on the one hand, and concerning linguistic meaning on the other. The centerpiece of the investigation into linguistic meaning is a proposal for an irreducibly social aspect of linguistic meaning, which I call the ‘public content’ of linguistic terms. This proposal is motivated by the identification of some points at which neither individualist models of linguistic meaning nor the ‘social’ models of meaning currently available give convincing accounts. Drawing on recent developments in social epistemology, I argue that this aspect of meaning is determined by what speakers engaged in discourse would agree on under an ideal process of collective reasoning as the meaning of the terms they use. In the last chapter I attempt to reconcile this model of meaning with the two-dimensional semantic model, arguing for a three-dimensional model of meaning that includes internal, external, and public dimensions. \nAlongside the discussion of linguistic meaning I explore a series of related puzzles that arise for conceptual content, particularly a new puzzle of referential indeterminacy, and the problem of conceptual error or normativity. I propose and defend solutions to these puzzles that lean heavily on the rational resources of individuals, focusing on the ‘personal level’ contents of thought to resolve puzzles in this domain, and rejecting models that lean on ‘sub-personal’ states such as neuronal, historical, or dispositional states of thinkers.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".