The Growth and Development of Institutional Reputation
Bibliographic record
Abstract
Higher education institutions spend considerable effort developing and maintaining quality educational programs and experiences for their students. However, these great programs and experiences can become best kept secrets if the institution is unknown. A positive reputation can contribute to student recruitment; successful career placement for graduates; greater retention of students, faculty, and staff; overall student satisfaction; and greater opportunities for the institution. A poor reputation, on the other hand, can negatively impact its success in recruitment; graduate career placements; and student, faculty, and staff retention. How does an institution develop and build a positive reputation and become more widely known and favourably regarded? This organizational improvement plan explores the theories and processes of leading an internationally focussed, private, for-profit degree-granting business school located in Vancouver, British Columbia, Canada through a process of change to enhance its reputation. The plan focusses on the role of leadership and the processes to successfully navigate change in an institution through the application of two leading change frameworks: the change path model and Kotter’s accelerate model for change management. This study demonstrates how authentic and distributed leadership theories are most appropriate for reputation development and applies social exchange theory to underpin recommended approaches and a strategy to build an institution’s reputation from the inside out.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".