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The Dark Side of Competition for Status

2013· article· en· 490 citations· W2029747413 on OpenAlex· 10.1287/mnsc.2013.1747

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Full frame distilled prediction

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.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: Theoretical or conceptualConsensus signal: none
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.731
Threshold uncertainty score
0.921
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.028
GPT teacher head0.330
Teacher spread
0.302 · how far apart the two teachers sit on this one work
Validation status
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

Abstract

Unethical behavior within organizations is not rare. We investigate experimentally the role of status-seeking behavior in sabotage and cheating activities aiming at improving one's performance ranking in a flat-wage environment. We find that average effort is higher when individuals are informed about their relative performance. However, ranking feedback also favors disreputable behavior. Some individuals do not hesitate to incur a cost to improve their rank by sabotaging others' work or by increasing artificially their own performance. Introducing sabotage opportunities has a strong detrimental effect on performance. Therefore, ranking incentives should be used with care. Inducing group identity discourages sabotage among peers but increases in-group rivalry. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1747 . This paper was accepted by John List, behavioral economics.

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.

The record

Venue
Management Science
Topic
Experimental Behavioral Economics Studies
Field
Social Sciences
Canadian institutions
Center for Interuniversity Research and Analysis on Organizations
Funders
Agence Nationale de la Recherche
Keywords
CheatingRanking (information retrieval)IncentiveRivalryCompetition (biology)Rank (graph theory)Social psychologyGreat RiftMicroeconomicsIdentity (music)Work (physics)WageOrganizational behaviorPsychologyEconomicsMarketingBusinessComputer scienceArtificial intelligenceLabour economicsEngineering
Has abstract in OpenAlex
yes