The role of critical qualitative research in educational contexts: A Canadian perspective
Bibliographic record
Abstract
ABSTRACT In this paper, the author discusses the qualitative shift in educational research from a historical dependence on positivist and post-positivist frameworks to a proliferation of research situated within a qualitative, interpretivist paradigm. Offering a Canadian perspective, the author situates the discussion of critical qualitative research in relation to the growing neoliberal influence on educational contexts and policy decisions. She argues the need for educational researchers to conduct critical qualitative research to explore the complex issues that educators face to ensure that all students have access to equitable educational experiences, not only those students who represent the Canadian dominant white, middle-class norm. The author introduces a strand of qualitative research focused on the experiences of black, racialized and Indigenous students to illustrate the need for critical research that privileges local knowledge and human experience, while also taking into account the dominant institutional structures that shape educational experience. Although acknowledging the place of quantitative research in the educational domain, she argues that critical qualitative research is essential to understand the experiences of marginalized students in educational contexts and to provide space for voices not available in the quantitative domain. The author concludes that critical qualitative research has an important role to play in informing new directions in educational practices and policies that will help to ensure a socially just education for all students, regardless of backgrounds.
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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 | 0.218 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.049 | 0.140 |
| Scholarly communication | 0.040 | 0.014 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".