Qualitative Insider Research in a Government Institution: Reflections on a Study of Policy Capacity
Bibliographic record
Abstract
Embarking on a qualitative Ph.D. research project in public administration is often daunting for novice researchers. For those students who consider adopting an emic or insider approach for their research, the ethical, methodological, and analytical challenges that lay ahead may seem insurmountable at times. In this article, I reflect on my experience as a Ph.D. student completing qualitative research with my colleagues to study policy capacity in a provincial government in Canada. I review how I constructed an ethical framework by integrating policy from Research Ethics Boards and government. Throughout the article, I deal primarily with ethical considerations and the personal and professional tensions associated with insider research. In addition to providing an overview of the literature on insider and emic research, I present ethical protocols that student-practitioners in other settings should consider when completing academic research with their colleagues in government institutions. Overall, the risks one must mitigate and minimize when completing insider research in government institutions are not substantially different from insider research in private institutions. While insider approaches in the study of public administration are not without their unique challenges, they do offer great potential in broadening and deepening emic knowledge of public administration practice.
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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 | 0.155 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.055 | 0.106 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".