Studying Strategizing through Biographical Interviews or Narratives of Practices
Bibliographic record
Abstract
Linda Rouleau suggests that biographical research provides a set of narrative methods of inquiry for carrying out in-depth studies of strategizing practices. Amongst the diverse forms that biographical methods can take, she suggests that biographical interviews or narratives of practices, that is, focusing on work experience and professional trajectories, provide privileged access to the subjective accounts of what managers and others ‘do’. Rouleau provides an overview of how biographical methods have been used in strategy as practice research in an attempt at gaining an in-depth look into the world of practitioners who are strategizing. She also puts forth illustrative data extracted from a previous study based on narratives of practices, which examined how middle managers deal with the restructuring of their organization. Finally, she explains how biographical methods, in general, and biographical interviews or narratives of practices, in particular, can be used to gain access to explicit and tacit knowledge, and how the depth of the relationship between narrator and researcher is central to a thorough understanding of strategizing practices.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".