Bibliographic record
Abstract
Our enthusiasm for Strategy as Practice should be evident in the chapters and commentaries that have preceded this one. This enthusiasm is similarly to be found in the community of academics that has grown up around it, as we indicate above. A good deal of this arises because Strategy as Practice provides a real opportunity to place an emphasis on people and what people do in relation to strategy – something which, as chapter 1 suggested, has been somewhat absent in much of what is now researched in the strategy field. We should however remember that this interest and enthusiasm is part of a long legacy. It builds on the legacy of the subject of strategic management back into the 1950s and 60s and it has been the continuing central concern of academics such as Henry Mintzberg, Robert Burgelman and Andrew Pettigrew. So this is not an entirely new perspective: it builds on and extends a tradition. Our objectives in this final part are twofold. First, we reflect collectively on the journey followed in this book, summarizing the central substantive, empirical and methodological themes developed in Parts I and II, and broadening the field of vision to draw in some of the most promising recent initiatives in this area. Second, each of the authors provides individual reflections on the contributions and future opportunities of Strategy as Practice, emphasizing those areas that are of greatest interest and concern to him or her personally. All of us have been independently involved in empirical work relevant to Strategy as Practice and were drawn to this area with slightly different motivations and interests.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.287 | 0.139 |
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".