Navigating Elite Interviews: The Experience of a Novice Researcher from a Non-Western Context
Bibliographic record
Abstract
Interviewing educational leaders in education poses a unique methodological challenge, especially for novice researchers, due to the different levels of experience between researcher and participants and the context of the research under investigation. These elites were highly influential, well-educated, and confident - work in organisations that carefully manage their public image, making access and relationship building difficult. For an inexperienced researcher, elite interviews offer a rare insight into the intricate interplay of influence, information control and strategic communication styles. This article looks at the experiences of a novice researcher conducting elite interviews with Malaysian educational leaders serving as college directors. Drawing on firsthand experiences, it highlights the challenges specific to the local context, such as institutional gatekeeping, hierarchical structures, and cultural expectations of deference. This article also discusses the lessons learnt and practical strategies necessary for navigating these obstacles, offering guidance for researchers engaging with elite participants. By shedding light on these nuanced realities, this article contributes to the broader discourse on elite interviewing as a qualitative research method and provides actionable insights for researchers working in culturally complex settings.
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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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".