Change in Higher Education: Understanding and Responding to Individual and Organizational Resistance
Bibliographic record
Abstract
In many fields, the ability of educators and practitioners to cope with rapid change is essential to sustained success. In veterinary medical education, as in other scientific disciplines, meaningful change is challenging to achieve and subject to resistance from many individual and organizational norms. Individual concerns often relate to fears of instability or uncertainty, loss of current status, or effects on individual time and workload. Sources of organizational resistance may include a conservative culture, fierce protection of current practices, and prevalence of disciplinary or territorial viewpoints. In academia, especially in scientific or medical fields, individuals appear to be strongly independent and conservative in nature, and generally skeptical of educational change. In this environment, a highly participatory process, with regular communication strategies and demonstrations or evidence that supports proposed changes, can be useful in facilitating change. An understanding of the nature of complex change, as well as of the reasons underlying resistance to change, and some methods to overcome these barriers are highly valuable tools for educational leaders.
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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| 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".