Evaluating Interpretive Inquiry: Reviewing the Validity Debate and Opening the Dialogue
Bibliographic record
Abstract
Designing and carrying out effective and valid research are the desired goals of all researchers, and demonstrating the trustworthiness of one's dissertation research is a requirement for all doctoral candidates. For qualitative researchers, reaching the desired goal and meeting the requirement of trustworthiness become particularly problematic due to the considerable debate about what it means to do valid research in the field of qualitative inquiry. This article reviews the various approaches to the validity problem in the hope of turning this debate into a dialogue. Validity is traced from its origins in the realist ontology and foundational epistemology of quantitative inquiry to its reformulations within the lifeworld ontology and non-foundationalism of interpretive human inquiry. Various recent qualitative approaches to validity are considered, and interpretive reconfigurations of validity are reviewed. Interpretive approaches to validity are synthesized as ethical and substantive procedures of validation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.408 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.032 | 0.036 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".