Analysis of Bilateral Effects between Social Undermining and Co-Creation among University Faculty Members
Bibliographic record
Abstract
<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>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".