Exploiting the Qualitative Potential of Q Methodology in a Post-Colonial Critical Discourse Analysis
Bibliographic record
Abstract
This conceptual article describes an approach I have taken when exploring the discourse associated with the teaching and learning of high school science in a given Caribbean location. Using a lens of post-colonial theory to guide the entire project, I employed an adaptation of the standard interpretation of Q methodology as part of a critical discourse analysis. In this article, I support and extend Shinebourne's (2009) representation of Q methodology as a means of “expanding the repertoire of qualitative research methods” (p. 93), as described in a previous issue of this journal. Given the challenging nature of the research theme and the analytic perspective that I employed as a researcher, the standard Q methodology protocol was augmented, whilst retaining the essential attributes of Q technique. This approach proved engaging for participants and was fruitful in providing insight into the tensions between shared and particular participant perspectives. The resultant research strategy described in this article would be of particular interest to researchers from a qualitative background, particularly those working within a post-foundational framework, who would value support in conducting a critical discourse analysis.
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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 | 0.133 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".