Bibliographic record
Abstract
Purpose The purpose of this paper is to add to the emerging literatures on organizational learning and strategic management by developing a practice perspective on strategic organizational learning (SOL). While the literature on SOL has been growing, much of it has targeted exclusively practitioners and has not yet elaborated the mechanics and the micro‐dynamics of SOL. This paper is an initial attempt at exploring two important aspects of SOL: deep‐structure politics, and sensegiving. Design/methodology/approach The paper reports a qualitative case study of a major construction project undertaken by a mid‐size urban university as a part of its strategic change initiative. Findings Several ways in which deep‐structure politics shaped SOL at the research site are highlighted. The findings suggest that deep‐structure politics and sensegiving can shape identity processes in the context of SOL in important ways, such as dramatically altering the identity of the project team and symbolically separating it from the host institution. Originality/value The paper enriches the predominantly practitioner literature on SOL with empirical examination of the practices of SOL.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".