Bibliographic record
Abstract
Leadership has two faces: an outward-facing, public, performative dimension as well as an inward-facing, private aspect. The emotional labour performed ‘behind the scenes’ by leaders often remains hidden from observation. Nevertheless, it exacts a toll on their wellbeing. Opportunities to gain insights into the socio-emotional toll experienced by leaders are therefore limited. This arts-based research stages that oft hidden drama in the form of a five character one-act play, or ethnodrama, created from anonymized data generated from semi-structured interviews with school principals in Canada. The data was first coded using emotional codes developed from the positive and negative affect schedule [PANAS] (Watson, Clark, & Tellegen, 1988). The most pronounced affects were incorporated into an original ethnodrama using the interview data and were subsequently validated by readers of the final artistic product. Stakeholders in education, leadership or the arts might engage in ethnotheatre, through performing or witnessing this work, to understand the hidden dynamism of the socio-emotional toll of school leadership. This article offers insights into the transdisciplinary intersections between leadership education and arts-based research, followed by the full script of the ethnodrama, and concludes with a description of the unique process through which data generated from classic, qualitative methods was artfully fashioned into The Two Faces of Leadership.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".