Exploring the leadership development of midlevel leaders within social work programs in Western Canadian universities.
Bibliographic record
Abstract
The purpose of this dissertation was to gain a deeper understanding of the leadership development experiences of midlevel leaders in social work programs at Western Canadian universities. Despite the increasing importance of midlevel leadership in academic institutions, a significant gap remains in research focused specifically on this group, underscoring the need for this study. Utilizing a narrative inquiry methodology and semi-structured interviews, the research involved seven participants, all of whom held midlevel leadership positions at Western Canadian universities. The conceptual framework included the social work leadership framework, attachment theory, and leadership development. Key findings revealed that participants described their leadership development as emerging through personal and professional relationships, transferable skills acquired from previous frontline social work practice or non-academic leadership roles, and experiential learning—developing leadership capacity while actively performing their roles within academia. Additionally, participants provided recommendations for enhancing leadership development pathways. These suggestions included topics for current and future midlevel leaders, along with proposed enhancements to bachelor’s and master’s level social work curricula to better prepare emerging leaders.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".