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Destructive criticism revisited: Appraisals, task outcomes, and the moderating role of competitiveness

2012· article· en· W6922268002 on OpenAlexaff

Bibliographic record

VenueThe Institutional Repository at DePaul University (DePaul University) · 2012
Typearticle
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsCriticismConstructive criticismBlameDistrustConstructiveTraitHarmTask (project management)Interpersonal communicationCognition

Abstract

fetched live from OpenAlex

Destructive criticism is negative feedback that is inconsiderate in style and content, which exists at the intersection of performance feedback and interpersonal mistreatment. The current research integrates these literatures with an investigation of the effects of destructive versus constructive criticism from a co-worker on recipients’ relational appraisals, emotions, and task outcomes. Drawing from theorizing about cognitive appraisals after personal affronts, we first propose that those who experience destructive criticism are more likely than those who experience constructive criticism to (a) perceive that the feedback-giver intended to harm them, (b) blame the feedback-giver, (c) distrust the feedback-giver, and (d) feel anger. Second, with regard to task-related outcomes, we extend research on trait moderators of feedback responses to the study of destructive criticism. We draw from feedback intervention theory (Kluger & DeNisi, 1996) regarding how feedback may alter the locus of attention to be either self- or task-focused, and investigate a trait that may shift one’s attention to the self after destructive criticism. Specifically, we proposed that trait competitiveness—i.e. a desire to win over others—interacts with type of criticism to predict task-related outcomes. The results of two experiments—a scenario study and a behavioral experiment—provide support for our arguments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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
Published2012
Admission routes1
Has abstractyes

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