Skookumchuck, Kiidk’yaas, Gibbard: normativity, meaning, and idealization (critical notice of Allan Gibbard <i>Meaning and Normativity</i>)
Bibliographic record
Abstract
Gibbard argues that ‘meaning is normative’. He explains the claim with an account of the normative which bases it on the process of planning, taken in part as issuing instructions to oneself. It seems to entail that the right kind of plans make norms. One ought to continue adding with plus rather than quus in a Kripkenstein horror story. I focus on Gibbard’s characterization of normativity: it is not what one might expect. The main purpose of this review article is to present the way of understanding normativity that makes most sense of what he says, and which makes some otherwise implausible assertions defensible and perhaps even true. I give reasons for thinking that Gibbard’s understanding of normativity-through-plans cannot do the work he wants it to. I also argue that he is onto something right, and it opens interesting new questions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".