Determinants of Faculty Retention: A Study of Engineering and Management Institutes in the State of Uttar Pradesh and NCR Delhi
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.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".