Bibliographic record
Abstract
This paper reports on a field study of strategy making in one organization facing an industry crisis. In a comparison of five strategy projects, we observed that organizational participants struggled with competing interpretations of what might emerge in the future, what was currently at stake, and even what had happened in the past. We develop a model of temporal work in strategy making that articulates how actors resolved differences and linked their interpretations of the past, present, and future so as to construct a strategic account that enabled concrete strategic choice and action. We found that settling on a particular account required it to be coherent, plausible, and acceptable; otherwise, breakdowns resulted. Such breakdowns could impede progress, but they could also be generative in provoking a search for new interpretations and possibilities for action. The more intensely actors engaged in temporal work, the more likely the strategies departed from the status quo. Our model suggests that strategy cannot be understood as the product of more or less accurate forecasting without considering the multiple interpretations of present concerns and historical trajectories that help to constitute those forecasts. Projections of the future are always entangled with views of the past and present, and temporal work is the means by which actors construct and reconstruct the connections among them. These insights into the mechanisms of strategy making help explain the practices and conditions that produce organizational inertia and change.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".