Bibliographic record
Abstract
Colour experiences are systematically related by phenomenal relativesimilarity, inclusion, and exclusion. Colour spaces model these experiences and relations. This dissertation argues that colour spaces are useful tools for modelling colour language meaning, especially with sensitivity to its metaphysical and epistemological dimensions. Knowledge of colour language involves associations among colour experiences, colour expressions, and colour properties. This includes associations between phenomenal relations among colour experiences and mirroring relations among the properties they represent. Colour spaces help model this knowledge. Chapter 1 ‘Colour Space Semantics’ argues that colour spaces also have another benefit. Facts about colour language meaning depend on facts about colour and colour spaces encode this dependence in our theory of meaning while simultaneously making possible a high degree of ii circumspection when theorizing about linguistic issues whose resolution depends on contentious theoretical questions about colour. One such question concerns the relation between phenomenology and representational content. If phenomenology and representational content “come apart” , different representational contents are possible for colour experiences of the same type, yielding different possible interpretations for colour expressions associated with it. As Chapter 2 ‘Languages and Colour Language’ argues, a certain independently appealing theory of colour forces us to consider what not only the meanings of colour expressions must be like, but also the languages they belong to. Colour spaces are crucial to the background theory that informs our analysis of colour language semantics. As Chapter 3 ‘Analyticity and Colour Language’ demonstrates, there may also be a role for them in the semantics. It argues that representing exclusion relations between colour properties in colour language meanings overcomes the standard objection that putatively analytic colour sentences resist analysis as such and supports following Russell (2008) in theorizing analyticity as a property that sentences have in virtue of their reference determiners, not their characters or truth conditions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".