Factors Affecting the Affective Identity-Motivation to Lead (AI-MTL) of Lecturers: Case Study in X Unversity
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to explore the effect of Leadership Self-Efficacy (LSE), Past Leadership Experience (PLE),Organizational Identification (OI), and Perceived Job Stress as an Academic Leader (PJSAL) on AffectiveIdentity-Motivation to Lead (AI-MTL) of lecturers at the X University simultaneously. This study also aims toexplore the role of LSE in mediating relationship between PLE and AI-MTL as well as between PJSAL and AI-MTL. A total of 125 X University lecturers participated in this study (male: 53, female: 72; age range between26-71 years old), with data collected through an online questionnaire. Data analysis then was performed using theHierarchical Multiple Regression and Mediation Analysis. The result shows that there is a simultaneous effect ofLSE, PLE, OI, and PJSAL, in predicting AI-MTL of lecturers at the X University, F(4, 120) = 63.520, p < .001.All variables can explain 67.9% of the AI-MTL variation, R2 = .679. Meanwhile, PJSAL does not provide anymeaningful contribution to the AI-MTL variation. In addition, this study also confirms the role of LSE inmediating the relationship between PLE and AI-MTL partially, c’ = 1.0508, p < .001, and fully mediating therelationship between PJSAL and AI-MTL, c’ = -.006, p > .05. These results emphasize the strong need to identifytalents by using those factors, especially when universities have difficulty in finding their prospective leaders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it