Foucauldian Discourse Analysis: Moving Beyond a Social Constructionist Analytic
Bibliographic record
Abstract
Although social constructionism (SC) and Foucauldian discourse analysis (FDA) are well established constructionist analytical methods, this article propose that Foucauldian discourse analysis is more useful for qualitative data analysis as it examines social legitimacy. While the SC is able to illuminate how the “meaning” of our social action is constructed through our everyday interaction in socio-cultural and political contexts, questions emerge that are beyond the scope of the SC. These questions are concerned with understanding how the construction of “meaning” is connected to the power imbalance in our society, as well as how a particular version of reality comes to us as truth, having excluded other versions. Moreover, SC does not distinguish between successful and unsuccessful/marginalized claims. This article reflects on how using FDA addresses weaknesses in SC when used in qualitative data analysis, using specific examples from different literature.
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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 | 0.108 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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".