Challenges and Best‐practice Recommendations for Designing and Conducting Interviews with Elite Informants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Elite informants (i.e., those in the upper echelon of organizations) are a key data source for building and testing theories in management research. We offer best‐practice recommendations to overcome challenges in designing and conducting interviews with elite informants (EIs) based on a comprehensive and multidisciplinary literature review and information provided by subject matter experts (i.e., authors of recently published articles that included EI interviews). Given unique characteristics of EIs and differences compared to interviewing non‐EIs, we provide recommendations on how to address challenges related to: (1) research design (e.g., what is the best order for the interviews?); (2) data collection (e.g., how can researchers access EIs? what is the best format for the interview? how can researchers obtain more honest responses?); and (3) reporting of results (i.e., what information should researchers report and to whom?). Finally, we offer suggestions for future EI research focusing on methodological issues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it