Studying leadership: an eclectic approach to qualitative data collection and analysis
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
Purpose This paper describes the approach to studying leadership within the interpretive paradigm that the author undertook for their doctoral research. Design/methodology/approach For the study, qualitative data were collected via in-depth interviews and the researcher's systematic reflections. The analytical process consisted of database design and management followed by cyclical interview and cross-interview analyses and integration of the researcher's reflections. Findings The paper showed that integrating various qualitative methods in pragmatic ways that better align with the research scope, along with a systematic approach to studying complex phenomena—such as leadership—generated richer and more cohesive perspectives. Research limitations/implications This paper provides a detailed account of how the qualitative data collection and analysis methods were used, which could be replicated or adapted for future research within the interpretive paradigm. Practical implications The paper helps students and junior researchers make sense of qualitative data analysis and interpretation by offering practical tools to tackle the diverse processes of qualitative research. Originality/value This paper is based on the Author's doctoral research, recording their experience with engaging in qualitative research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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