Eco-Normalization: Evaluating the Longevity of an Innovation in Context
Bibliographic record
Abstract
PURPOSE: When initiating an educational innovation, successful implementation and meaningful, lasting change can be elusive. This elusiveness stems from the difficulty of introducing changes into complex ecosystems. Program evaluation models that focus on implementation fidelity examine the inner workings of an innovation in the real-world context. However, the methods by which fidelity is typically examined may inadvertently limit thinking about the trajectory of an innovation over time. Thus, a new approach is needed, one that focuses on whether the conditions observed during the implementation phase of an educational innovation represent a foundation for meaningful, long-lasting change. METHOD: Through a critical review, authors examined relevant models from implementation science and developed a comprehensive framework that shifts the focus of program evaluation from exploring snapshots in time to assessing the trajectory of an innovation beyond the implementation phase. RESULTS: Durable and meaningful "normalization" of an innovation is rooted in how the local aspirations and practices of the institutional system and the people doing the work interact with the grand aspirations and features of the innovation. Borrowing from Normalization Process Theory, the Consolidated Framework for Implementation Research, and Reflexive Monitoring in Action, the authors developed a framework, called Eco-Normalization, that highlights 6 critical questions to be considered when evaluating the potential longevity of an innovation. CONCLUSIONS: When evaluating an educational innovation, the Eco-Normalization model focuses our attention on the ecosystem of change and the features of the ecosystem that may contribute to (or hinder) the longevity of innovations in context.
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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 | 0.245 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".