Arts-Based Instructional Leadership: Crafting a Supervisory Practice that Supports the Art of Teaching
Bibliographic record
Abstract
If teaching at its best is an art (Davis, 2005; Sarason, 1999; Grumet, 1993; Eisner, 1985; Barone, 1983; Greene, 1971; Smith 1971), then instructional leadership of teaching, done best, must also be based in art (Behar-Horenstein, 2004; Klein, 1999; Eisner, 1983 & 1998a; Blumberg, 1989; Barone, 1998). The author examines possible applications of an arts-based approach to instructional leadership (Blumberg, 1989; Pajak, 2003; Barone, 1998). Building on the research base regarding instructional leadership as art form, the author combines the Feldman Method (Feldman, 1995) of critique, Eisner’s (1998) notion of connoisseurship and Ragans’ (2005) articulation of the elements of art and the principles of design to construct a practice that captures both the technical craft of teaching and the aesthetic dimensions evident in artistic pedagogy (Eisner, 1983; Sarason, 1999). Preliminary results of an ongoing implementation study are presented.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".