Looking desperately for courage or how to study a polysemic concept
Bibliographic record
Abstract
Purpose The purpose of this article is to propose an approach using mixed methods appropriate for studying polysemic concepts. Design/methodology/approach Anchored in cognitive approaches, the methods relied on a generally applicable conceptual framework, on cognitive mapping for an intellectualized conception, and on in‐depth interviews for an experiential conception on different participants’ judgments of managerial courage within the same context. Findings The mixed methods approach allowed the study first, to uncover two kinds of managerial courage. Second, while the intellectualized conceptions led to the enumeration of a greater number of positive consequences for third parties, the conceptions resulting from recollections of experiences focused more on the consequences for the protagonist. Third, the conceptual framework allowed the authors to distinguish between the results obtained from the two distinct data collection methods: the moral dimension, present in the more intellectualized cognitive maps, was largely absent from the consequences identified by participants in the conception of managerial courage resulting from experience. Originality/value This approach has provided two original methodological contributions. The first is the development of a widely applicable conceptual framework useful for studying polysemic concepts and for treating data generated by both approaches. The second is the distinction between conceptions of courage obtained from cognitive maps and those obtained through semi‐structured, in‐depth interviews, highlighting the complementarity of the chosen methods.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".