Bibliographic record
Abstract
Halkomelem is a Central Salish language of the Pacific Northwest with dialects of Upriver, Downriver, and Island. Color terms are adjectival verbs in Upriver Halkomelem, used syntactically and cognitively as both verbs and adjectives. Fieldwork with Munsell chips reveals that speakers of Upriver Halkomelem use a variety of strategies to name shades of color. Galloway 1992 reported on the use of aspect inflection to name the shades; thus color term roots are inflected for stative (be red), inceptive (get red, turn red), and these can be combined with continuative aspect (being red, getting/tuming red, getting in a state of red) and even with diminutives (a little red, getting/tuming a little red). There is one augmentative affix but adverbs like ‘very’ and ‘real’ are preposed by certain speakers to express some shades. Color adjectives, with or without adverbs and affixes, may be used sentence-initially as verbs or may be preposed to nouns within noun phrases. It seems, then, that different speakers use distinct metaphorical cognitive models in creating these terms. All speakers share the metaphorical model that COLORS ARE PROCESSES. This cognitive model allows further metaphors to be applied, for example, INCREASING SATURATION & BRIGHTNESS ARE CONTINUATIVES. Another metaphorical model is seen with the diminutive: DECREASING SATURATION & BRIGHTNESS ARE DIMINISHING IN SIZE OR AMOUNT. These last two mental models are coherent with each other and, therefore, can be used together. There are also adjectives meaning ‘light’ and ‘dark’ which some speakers use with color terms, and some use two basic color terms together to express variations in hue (like blue-green in English). Investigation continues into whether there are submodels ofmodifiers used to express variations in saturation independently from those in brightness. In Upriver Halkomelem, thinking of color in terms of culturally sanctioned metaphors is a convention that exceeds mere application of linguistic form to unprocessed color perception. Communicative subtlety depends on them.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".