Determinants of Faculty Retention: A Study of Engineering and Management Institutes in the State of Uttar Pradesh and NCR Delhi
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quality education is absolutely essential for the overall development of the human resource base of a country. This requires imparting of appropriate knowledge, skills and values to the students. To achieve this faculty is the main source and instrument. In the present scenario where engineering and management institutes have increased manifold in last two decades, an imbalance between demand for qualified and trained faculty and its supply has emerged. In this situation, the recruitment and retention of talented faculty becomes crucial. However, due to demand exceeding the supply, heavy faculty turnovers is being observed in recent years. The present study examines the major factors on which the retention of faculty depends. To identify the factors on which faculty retention depends, the existing literature has been thoroughly examined and the important factors have been identified. Based on these factors, a questionnaire has been developed, whose reliability and validity has been tested. The developed questionnaire has been administered on management and engineering institutes operating in U.P. and N.C.R. Delhi. Exploratory Factor Analysis (EFC) technique has been used to identify the most significant factors affecting faculty retention. The results of the study could be used by management and engineering institutes to devise strategies for effective use of faculty and their retention.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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