Classic Theories – Contemporary Applications: a comparative study of the implementation of innovation
Bibliographic record
Abstract
In a classic article reviewing the field of planned change, Chin and Benne (1984) outlined three meta-approaches to the implementation of change in social and organizational contexts. These meta-theories of change – Rational-Empirical, Normative-Reeducative, and Power-Coercive – summarized the field of then-existing knowledge related to innovation at a systems level. Chin and Benne’s resulting framework summarized much of the practice in change management carried out to date and provided a framework for planning of change to be implemented over the next several decades. Research with a Public Sector focus carried out more recently (Popovich, 1998; Pozner and Rothstein, 1994) confirms that the theoretical orientations outlined in 1984 continue to be applied to the practice of innovation among modern public sector managers. This study outlines the relative popularity of each of the three meta-strategies within public sector environments within Canadian and Chinese Public Sector environments. Research interviews and literature indicate that there are significant differences, driven by culture and experience with change itself, across these two environments. Within the framework provided by Chin and Benne, current practice in a Canadian environment tends to favour a combination of Normative-
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".