Bibliographic record
Abstract
Abstract Temperature adjectives serve as linguistic vehicles to convey sensory perceptions of physical temperature and metaphorical extensions that resonate across various spheres of communication. Languages vary widely in their vocabulary related to temperature, how they classify these terms, and where they fit within grammatical structures. Despite this diversity, there are shared features in how temperature adjectives convey both temperature and metaphorical meanings across languages. However, European Portuguese (EP) research on temperature adjectives remains scarce. To better understand temperature adjectives in EP, we conducted a corpus-based analysis of eight adjectives -“gélido”, “gelado”, “frio”, “fresco”, “morno”, “tépido”, “quente”, “escaldante” (icy, frozen, cold, cool, lukewarm, tepid, hot, scorching) — to investigate their literal and metaphorical meanings using a corpus of 2920 fragments from the Reference Corpus of Contemporary Portuguese and a quantitative and qualitative approach for the analysis. Results show that all adjectives have metaphorical meanings in addition to basic temperature interpretations, but their distribution varies. They primarily appear post-nominally and in attributive positions, responding differently to degree quantifiers. While the nouns with which the adjectives combine are relevant, the alternation between temperature and metaphorical readings also depends on context.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".