Rethinking Validity in Qualitative Research from a Social Constructionist Perspective: From Is this Valid Research? To What Is this Research Valid for?
Bibliographic record
Abstract
This article theorizes the issue of validity that is premised upon social constructionist assumptions, particularly as it is applied to the assessment of qualitative research. As a social construction, validity must thus be interrogated for its discursive function within the social sciences. I will argue that, as a criterion of assessment, validity polices the social science enterprise and thus, functions as a practice of power through the de/legitimation of social knowledge, research practice, and experiential possibilities. This critique will lead into a reformulation of validity that actively recognizes and negotiates its practice of power. Within this reformulation, research findings are conceptualized as representations and should be scrutinized for their realist, critical, deconstructive, and reflexive narrative function. Put simply, assessing qualitative research entails multiple and contradictory readings of its representational failures and successes. Therefore, validity is no longer conceived as a determination (i.e., is valid versus is not valid) but a continual process of interrogation. This new framework will be applied to my Masters thesis research that explored domestic violence and relationship abuse among gay males. Implications for research practice are discussed.
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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 | 0.760 | 0.729 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.029 | 0.291 |
| Scholarly communication | 0.045 | 0.069 |
| Open science | 0.011 | 0.036 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".