Bibliographic record
Abstract
Abstract The paper deals with words that denote feelings rather than with feelings as such. It proposes the strictly lexicographic description of some names of ‘psyche-induced feelings1’ [= ‘feelings2’], such as joy or amazement, in contrast to the names of ‘body-induced feelings1’ [= ‘sensations’], such as hunger and tiredness. This description is based on the semantic prime ‘feel1’, which itself is explicated through a naïve model of the human psyche. Our theoretical and descriptive framework is the Explanatory Combinatorial Dictionary: its main principles, the notions of lexical unit (described by a lexical entry) and vocable (described by a lexical superentry), and the three major zones of a lexical entry. A tripartite general schema of the lexicographic definition of a feeling2 name is proposed: the central (= generic) component, the Stimulus component, and the Effect component. According to the Stimulus component, four major classes of feeling2 names are distinguished: names of reactions to facts, to thoughts, to beliefs, and to wishes. These classes are illustrated with the definitions of several English feeling2 names. A complete lexical entry for the feeling2 name anger(N)1 is given.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".