Learning Is Change: Creating an Environment for Sustainable Organizational Change in Continuing and Higher Education
Bibliographic record
Abstract
This article explores the ways in which learning itself is a form of organizational change and, as such, supports organizational readiness for change. The study considers a continuing education unit within a major Canadian university that managed to transform its decentralized and independent student records and administration system (student registration, student financials, student academic records) by merging into the university’s central student management system.The technological implementation and transformation took place over 18 months and was enabled by a series of formal committees and working groups, involving a wide range of members across the university’s communities and within the continuing education unit. The empirical data consist of responses given during in-depth interviews with a set of participants involved in the change initiative and technology implementation. Managers’ reactions to and reflections on organizational change figure prominently in the research findings and discussion.The article aims to show that creating an environment for sustainable organizational change in higher education, and perhaps more generally, is supported by recognizing that learning itself is change, and that workplace learning may therefore help to create organizational readiness for change.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".