Sensemaking in Organizations: Taking Stock and Moving Forward
Bibliographic record
Abstract
Sensemaking is the process through which people work to understand issues or events that are novel, ambiguous, confusing, or in some other way violate expectations. As an activity central to organizing, sensemaking has been the subject of considerable research which has intensified over the last decade. We begin this review with a historical overview of the field, and develop a definition of sensemaking rooted in recurrent themes from the literature. We then review and integrate existing theory and research, focusing on two key bodies of work. The first explores how sensemaking is accomplished, unpacking the sensemaking process by examining how events become triggers for sensemaking, how intersubjective meaning is created, and the role of action in sensemaking. The second body considers how sensemaking enables the accomplishment of other key organizational processes, such organizational change, learning, and creativity and innovation. The final part of the chapter draws on areas of difference and debate highlighted throughout the review to discuss the implications of key tensions in the sensemaking literature, and identifies important theoretical and methodological opportunities for the field.
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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 | 0.033 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.030 | 0.099 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".