Short-term informal leadership learning : a critical realist case study
Bibliographic record
Abstract
Uzochukwu Jude UDEDIBIA, B.Phil., MCE, Short-Term Informal Leadership Learning (STILL): A Critical Realist Case Study Research, submitted to Lancaster University Management School for the degree of Doctor of Philosophy (PhD) in Management Learning and Leadership, September 2019. This thesis focuses on the short-term informal learning from the restructuring experience of twenty four senior leaders from three Canadian healthcare organizations who led restructuring in their organizations. This study investigated the leaders’ learning through a critical realist case study research approach. It used Pawson and Tilley’s (1997) Context-Mechanisms-Outcomes (CMO) framework to analyze their learning by describing the context of the learning, inferring mechanisms that can plausibly explain their learning, and describing the learning outcomes that these inferred mechanisms produced. This research’s departure point is the ongoing issue and debate about how formal leadership learning only contributes marginally to leadership emergence and development. In practice, organizations are complaining that they are not seeing acceptable returns in the heavy investments they have made in leadership development programs. On their own side, leadership scholars, having noticed this contribution gap, are calling for more research that may potentially contribute more to leadership learning. Therefore it is valuable to focus research on other sources - as processes or models - that have the potential to contribute more to leadership development. One such area for investigation is understanding how leaders learn informally in the short-term given time pressures occasioned by fast-moving organizational change demands. While previous research identified that learning informally from the experience of activities that they are engaged with is a natural way for leaders to learn, focus on this had remained on the learning that occurs over a long period of time. As insufficient research attention has been given to this area of how short-term informal leadership learning can occur, this research undertook this research to contribute to knowledge in this underserved area of leadership development. This research found that leaders can learn in the short-term and that a model that involves the processes of attention grabbing, rapid reaction, and meaning making can explain how the learning occurs.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".