Stories Matter: Reaffirming the Value of Qualitative Research
Bibliographic record
Abstract
While the social sciences are experiencing narrative and emotional turns that are largely based on exploratory and theoretical qualitative research, the problematic dismissal of qualitative research approaches continues to loom large outside academia. Frequently described as a collection of “anecdotal stories,” qualitative research is dismissed as unscientific and unreliable— comments that limit the perceived usefulness of qualitative findings, especially in terms of policy reform. This article problematizes evaluating qualitative research according to quantitative measures of rigour and explores the richness and value of documenting experiential stories and the process of storying in social science research. Notably, we take up the issues of criminal record suspension (pardons) and the abolition of carceral segregation as two case studies to demonstrate how the qualitative value of experiential research and personal stories are simultaneously mobilized and rejected by key actors such as politicians, government researchers, and judges. Our analysis highlights the power that stories have when it comes to influencing change within the criminal justice system, depending on who takes up/rejects these stories. We conclude with a discussion of why stories matter and how, when “layered,” they can contribute to the production of meaningful interventions to the ongoing criminalization and punishment of vulnerable people.
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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 | 0.647 | 0.678 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.021 | 0.171 |
| Scholarly communication | 0.047 | 0.071 |
| Open science | 0.011 | 0.042 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".