Cognition and Change: Uniting Dynamic Cognitive Perspectives
Bibliographic record
Abstract
Cognitive processes, such as analogical reasoning, attention, and search, are central to scholars’ understanding of organizational change. Despite a deeper understanding of various cognitive processes at play within an organization, additional integration is needed between these cognitive mechanisms and organizational outcomes, such as learning and adaptation, as most research has focused failure of firms to change in the light of radical environmental disruptions. Additionally, additional work is needed to explore the interplay between theories of cognition and other major theories in strategy, such as capabilities and behavioral theory. This symposium brings together recent work that explores the dynamic between managerial cognition and organizational change in new contexts, enriching our understanding of the mechanisms through which cognitive processes facilitate change, inhibit change, and evolve themselves. We feature two discussants: Pamela Barr to discuss the need for cognitive approaches on change and Sarah Kaplan to provide her own insights about existing work and critical questions for the future research in this area. We conclude with audience feedback and more general discussion. The Role of the Business Analytics Community in Fostering Change-Oriented Agency Presenter: Christopher William John Steele; Northwestern Kellogg School of Management Presenter: William Ocasio; Northwestern U. Lessons Not Learned: The Cost of Forgetting Analogies Presenter: Natalya Vinokurova; U. of Pennsylvania A Cognitive View of Routine Change: Evidence from an Electronic Health Record Implementation Presenter: Alex James Wilson; Duke U. Presenter: John Joseph; U. of California, Irvine
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".