Towards Leading Adaptable Colleges: A Description of the Potential for Experimentation in Three British Columbia Colleges
Bibliographic record
Abstract
In this research, three British Columbia colleges were studied to understand how executive teams led innovation and enabled or failed to enable experimentation in an economic climate of decreasing funding. By creating a description of these teams, the author then concludes about the generative adaptive capacity of the colleges in the context of a challenging economic environment. Each college was described and interpreted as a separate case, and the researcher presents an integrated framework from an analysis of the findings and relevant literature on leading high capacity institutions. The most relevant literature for a study of this context was found to be complexity theory. Here, the author uses a new conceptual frame applied to describe and to interpret organizational culture and leadership approaches. The research found that none of the cases studied provide strong evidence of an executive team succeeding in creating adaptation through innovation via novel experimentation. As a consequence, the author developed a new conceptual model and presents implications to help guide executives to meet the challenges related to organization adaptability when they face change in the future.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".