Developing a Model for Promoting Professional Ethics of Faculty Members at the Islamic Azad University in the Dimension of Education
Bibliographic record
Abstract
The current research aimed to develop a model for promoting professional ethics of faculty members of the Islamic Azad University. For this, the most important components and areas of professional ethics were identified; also, relationship between professional ethics and some demographic characteristics, including gender, age and educational major were addressed. To meet this goal, a sample of education practitioners, faculty members and doctoral students was selected via a purposive sampling method for the qualitative part of the research. Also, via studying documents and interviews conducted with the academic practitioners and doctoral students, professional ethics related components were identified, and accordingly, an inventory consisting of 81 items was prepared and was administered to 600 faculty members of Azad University. Factorial analysis results indicated that the individual area consisted of 4 components, which explained 78/53% of its variance in total; organizational area consisted of 3 components which explained 68/96% of its variance and one factor was obtained within the environmental area that explained 86/82% of its variance. Results of structural equations modeling suggested that the recommended conceptual model enjoyed some acceptable goodness of fit with data. Findings pertaining to the validation of the model, including five parts (philosophy and objectives, theoretical basics, perception framework, administrative stages and evaluation system) illustrated that the model enjoys good validity from the views of experts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".