If you aren’t talking about practices, don’t call it a practice-based view: Rejoinder to Bromiley and Rau in <i>Strategic Organization</i>
Bibliographic record
Abstract
In this debate about the value of introducing a supposed “practice-based view of strategy,” we respond to Bromiley and Rau’s defense of their approach. Coming from a background of two decades of research on strategy-as-practice, we focus on two major concerns about their initiative. The first is that the very use of the term “practice” would seem to obfuscate more than elucidate, especially given their definition of “practice” which strongly deviates from that already established in the social sciences generally. The second is that, by applying the term “practice” to strategy specifically, it becomes incumbent upon Bromiley and Rau to engage with and build upon the extensive practice-related strategy research that has gone before them.
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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 | 0.045 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.026 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".