Bibliographic record
Abstract
Changing Subjects of Action ResearchI realized in the midst of writing this foreword that I am completing my fortieth year in the field of education -ten years as a classroom teacher and thirty in higher education, the last ten as dean of education.Reading through the chapters in this volume has been both a reminder of the important ways that Canadians have advanced the field of educational action research and also of how we have provoked that field.As I and others have argued elsewhere (2001), the co-emergence of curriculum and nation has inspired in Canada an openness to bibliographic inclusion in both our theoretical and methodological work.One needs only to review the citation lists of the chapters in this volume to notice how this continues to be the case.While I am cautious about essentializing the identities and work of Canadian researchers, I also believe it is important to note how our ability to value difference and diversity in our approaches to research -including educational action research -has resulted in important changes in schools, universities, and communities in Canada and elsewhere.Of course, the strength of any approach to research is its particularity -that which distinguishes it from other forms.This requires not only technical knowledge about methodological procedures but also strong understanding of the theoretical and conceptual knowledge and assumptions guiding those processes.As important -and perhaps, one could argue, most important -is knowledge of the histories of the emergence and evolution of the research methods/approaches used, with particular attention to how they were used in the specific context of historical times and situations.Most impressive and important about educational action research in Canada over the past several xii
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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 | 0.107 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.024 | 0.216 |
| Scholarly communication | 0.043 | 0.027 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".