The Structure of Deference: Modeling Occupational Status Using Affect Control Theory
Bibliographic record
Abstract
Current theories of occupational status conceptualize it as either a function of cultural esteem or the symbolic aspect of the class structure. Based on Weber’s definition of status as rooted in either cultural or class conditions, we argue that a consistent operationalization of occupational status must account for both of these dimensions. Using quantitative measures of cultural sentiments for occupational identities, we use affect control theory to model the network deference relations across occupations. We calculate a measure of the extent to which one occupational actor deferring to another is incongruent with cultural expectations for all possible combinations of 304 occupational titles. Because high-status actors are less likely to defer to low-status actors, the degree to which these events violate cultural expectations provides an indicator of the relative status position of different occupations. We assess the construct validity of our new deference score measure using Harris Poll data. Deference scores are more predictive of status rankings from poll data than are occupational prestige scores. We establish criterion validity using five theoretically relevant workplace outcomes: subjective work attachment, job satisfaction, general happiness, the importance of meaningful work, and perceived respect at work.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".