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Record W2047742801 · doi:10.1002/hrm.20278

Taking advantage of social comparisons in performance appraisal: The relative percentile method

2009· article· en· W2047742801 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Resource Management · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsToronto Public HealthUniversity of GuelphUniversity of Prince Edward IslandWestern University
Fundersnot available
KeywordsPercentilePsychologyPerformance appraisalVariance (accounting)Social comparison theoryPercentile rankAssessment centerSocial psychologySample (material)Absolute deviationStatisticsApplied psychologyAbsolute (philosophy)EconometricsMathematicsEconomicsManagement

Abstract

fetched live from OpenAlex

Abstract Social comparison theory (Festinger, 1954) implies that it may be more efficacious for job performance raters to compare an employee to other employees rather than to use typical “absolute” rating standards. We assessed whether the incorporation of social comparisons into performance appraisals, using the relative percentile method (RPM), would predict criterion variance beyond that predicted by more traditional absolute ratings of performance. A sample (N=170) of managers involved in an assessment center was used, and the center provided criteria by which the relative criterion‐related validity of social‐comparative versus noncomparative (absolute) appraisals could be assessed. Overall, in consonance with a preponderance of earlier research, social‐comparative (RPM) performance appraisals showed incremental criterion‐related validity over traditional absolute performance appraisal methods. © 2009 Wiley Periodicals, Inc.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.530

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.422
Teacher spread0.373 · 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