The Changing of the Guard in Academia and Academic Research Leadership—Employing Natural Language Processing
Bibliographic record
Abstract
This pioneering study examines the meaning of academic leadership in terms of the changing of the guard in academia. Research findings on seniority and experience and their association with leadership show that these have a considerable impact on management skills and on the ability of those with experience and seniority to influence the young leadership. This is particularly essential in academia where research is the most meaningful and effective value that serves as a measure of faculty members. Management skills are not perceived as a coherent part of faculty members’ work. Structural Equation Modeling confirmed the developed model. Findings show that indeed, from the perspective of faculty at the academic institution, senior experienced faculty members undoubtedly contribute to the academic institution first of all in research, but also otherwise. Senior and experienced faculty members contribute by encouraging, directing, and guiding young faculty members on how to contribute to the institution, particularly through the activity which is expected of them as academic faculty – i.e., research. This urging and direction is one of the most well-known qualities in the context of academic leadership – the ability to help people develop, advance, and to outline a high-quality academic research tradition. The meaning of the findings is that senior faculty has a contribution beyond their direct output in the form of scientific publications, as a research engine and spotlight for the young faculty. Notably, no difference was found in faculty’s perception of this contribution of senior faculty members by gender or age.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".