Visualizing Inclusive Leadership: Using Arts-based Research to Develop an Aligned University Culture
Bibliographic record
Abstract
Values of exclusive leadership characterize the administration of the neoliberal university, but are incongruous with values of inclusive leadership often enacted in the work of teaching, learning, and research. This article explores how an action research project to advance inclusive leadership at Royal Roads University adapted a visual data elicitation method and used metaphor analysis to reveal opportunities to align espoused, communicated, and enacted values. Images evoke metaphors (Mumby & Spitzack, 1983; Vakkayil, 2008) that enable researchers engaged in their own organizational development to elicit creative possibilities that are “covered up by the familiarity of everyday experience” (Koch & Deetz, 1981, p. 13). By eliciting desired qualities associated with inclusive leadership (Rayner, 2009), we have been able to make visible and model inclusive messages, structures, behaviours, strategies, and actions as the building blocks of a culture built on the value of inclusivity and collaboration, and the principles of diversity and interdependence. One key insight of the research is that arts-based action research effectively equips academic and administrative leaders to transcend deficit-based problem solving and the reductionism associated with neoliberal university management and to approach organizational development with the creative energy that arts-based research inspires.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".