Theorizing Affective Motivation: Motives, Seeking, and the Mundanity of Pleasure
Bibliographic record
Abstract
A central issue in sociology concerns motivation. Generally, sociologists have followed Mills’s lead in emphasizing motive-talk, or post hoc explanations, over the “springs” of action themselves. Drawing from the interdisciplinary science of motivation, this article argues that we can tease motives apart from motive-talk by incorporating the affective disposition to seek into a theory of motivation. Seeking involves three dissociable phases—wanting, liking, and learning—each of which is intrinsically pleasurable. This suggests three things. First, mundane activities related to anticipation or learning are affective in nature. Second, not only does each phase of seeking involve different motives, but also, any given phase contains sequences of activities, likely indicating mixed motives. And third, people commit to routines and roles not because of their habits or automatic cognition but because of the affective urgency and expectation they feel in their bodies. Implications for the sociological literature on pleasure and for a sociology of motivation 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 | 0.004 |
| 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 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".