The Renewal of a Concept: Analyzing “Innovation” Through The Five Contexts Framework
Bibliographic record
Abstract
In Cooper’s and White’s “Distinguished Performances: The Educative Role of Disciplines in Qualitative Research in Education” published in the International Review of Qualitative Research (2009), Cooper and White describe programs of research that led them to develop “The Five Contexts,” a analytical framework for conducting, understanding, and interpreting qualitative research in education and in other disciplines (169). That article ends with a question: “How can we delve more deeply into The Five Contexts to develop greater understanding of issues that cross disciplinary lines?” (185). This paper applies The Five Contexts framework to a theoretical analysis of the term “innovation”. As education researchers embarking on a research project on innovation in social sciences and humanities education, Cooper and Waterman view innovation literature, especially the work of Benoît Godin, through the lenses of The Five Contexts—autobiographical, historical, political, postmodern, and philosophical. The discussion provides a critical analysis of the term “innovation” in the educational context. It suggests that the concept of innovation in education may be due for a renewal, in the sense of a return of the old as new and a broader concept of innovation beyond information technology, which is becoming status quo. This paper provides an example of employing The Five Contexts analytical framework as a tool for theoretical analysis, with “innovation” as the concept being explored.
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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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".