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Record W6982610995

Is this really kneaded? Identifying and eliminating potentially harmful forms of workplace control

2024· other· en· W6982610995 on OpenAlexfundno aff

Bibliographic record

VenueKölner Universitäts PublikationsServer (Universität zu Köln) · 2024
Typeother
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversität zu KölnDeutsche Forschungsgemeinschaft
KeywordsChecklistAttritionControl (management)GermanValue (mathematics)Perception
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.198
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0500.002

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.

Opus teacher head0.026
GPT teacher head0.292
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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