Is this really kneaded? Identifying and eliminating potentially harmful forms of workplace control
Bibliographic record
Abstract
In a large German bakery chain, many workers report negative perceptions of monitoring via checklists. We survey workers and managers about the value and time costs to all in-store checklists, leading the firm to randomly remove two of the most perceivedly time-consuming and low-value checklists in half of stores. Sales increase and store manager attrition substantially decreases, and this occurs without a rise in measurable workplace problems. Before random assignment, regional managers predict whether the treatment would be effective for each store they oversee. Ex post, beneficial effects of checklist removal are fully concentrated in stores where regional managers predict the treatment will be effective, reflecting substantial heterogeneity in returns that is well-understood by these upper managers. Effects of checklist removal do not appear to come from workers having more time for production, but rather due to improvements in employee trust and commitment. Following the RCT, the firm implemented firmwide reductions in monitoring, eliminating a checklist regarded as demeaning, but keeping a checklist that helps coordinate production.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".