The content and structure of laypeople's concept of pleasure
Bibliographic record
Abstract
Five studies were conducted to map the content and structure of laypeople's conceptions of pleasure. Instances of the pleasure concept collected in Study 1 consisted predominantly of objects, events or persons described as sources of pleasure. Content analysis suggested that the pleasure category, like emotional response categories, might be formed at an implicit level where various pleasure antecedents are grouped based on common phenomenological qualities of the affective experience. Studies 2 and 3a showed that the pleasure category possesses a graded structure and fuzzy boundaries. Results further revealed that, either when explicitly presented with labels (Study 3b) or left to their own implicit categories during a sorting task (Study 4), laypeople represented pleasure as a hierarchical concept in which differentiated pleasure types (i.e., intellectual, emotional, social and physical) were subsumed under a higher level unitary form of pleasure. In this structure, unitary and differentiated pleasures shared a set of common affective qualities but were also distinguishable by unique and distinctive affective characteristics (Study 5). Ties to prior theories of pleasure and implications for decision making and behavioural research are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".