The Dark Side of Routine Dynamics: Deceit and the Work of Romeo Pimps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this chapter, the authors examine the use of deceit to drive routine emergence. The authors do so by tracing the relationship among deceit, roles, and routine dynamics in the context of Romeo pimps and the women they lure into sex trafficking. Previous research has focused on routine participants openly negotiating their roles and expected interactions during the (re) creation of routines. In contrast, this study shows how Romeo pimps use deceit to control the co-constitution of roles and increasingly coercive actions of the “Romeo pimp routine” – a process of premeditated routine emergence designed to entrap the women. The authors contribute to the literature on routine dynamics by emphasizing the unexplored influence of deceit on the interplay between roles and routines. Bringing deception to center stage in routine dynamics highlights the importance of linking actors and actions to motivations that exist behind the veil of transparently observable behavior.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it