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Record W2471678335 · doi:10.5539/ies.v9n7p135

Analysis of Bilateral Effects between Social Undermining and Co-Creation among University Faculty Members

2016· article· en· W2471678335 on OpenAlexvenueno aff
Fatima Taherpour, Saeed Rajaeepour, Ali Siadat, Iraj Kazemi

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaPsychologyStratified samplingSocial psychologySample (material)PopulationApplied psychologyDevelopmental psychologySociologyPsychometricsDemographyStatistics

Abstract

fetched live from OpenAlex

<p class="apa">Understanding the social undermining is increasing important in organizational literature both because of its relation with job performance and because of its collective cost to individuals and organizations. This article argued that social undermining can effect on co-creation among faculty members. The study adopted a descriptive–correlational method. The statistical population of the study consisted university faculty members in Iran, that 235 members were selected as the participants using stratified random sampling consistent with the sample size. Social undermining was examined using Duffy et al. (2002) Questionnaire and co-creation were examined using researcher-made questionnaire based on co-creation DART model. Also the reliability of the questionnaire were computed using Cronbach’s alpha (0.94 for social undermining and 0.96 for co-creation questionnaire).Results based on data from a sample of university faculty members showed a negative relationship between social undermining and co-creation and were meaningful at 0.05 level of significance.</p>

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.050
GPT teacher head0.363
Teacher spread0.312 · 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 designObservational
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

Citations5
Published2016
Admission routes1
Has abstractyes

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