Documenting and Activating Educational Leadership and Authentic Teaching
Bibliographic record
Abstract
This essay describes two integrated projects initiated by the 2020 3M National Teaching Fellowship Award (NTF) cohort on educational leadership and the role of authenticity among exemplary teachers, as presented at STLHE 2022. A thematic analysis of 3M NTF award-winning dossiers identified six prevalent traits characteristic of educational leaders: innovation, persistence, responsiveness, reflectiveness, curiosity, and positive opportunism. The analysis also revealed aspects of educational leadership in practice, including being committed to a cause, being action-oriented, being community-engaged, being multi-disciplinary, building bridges, freely sharing, trailblazing, and using applied methods. Educational leaders’ relationships with others tended to foreground elements of collaboration, empowerment, support, and mentorship, and their actions had an impact beyond their own classrooms or institutions. In the second project, qualitative interviews with cohort members articulated ways in which authentic teaching is expressed by educational leaders. The actions of authentic teachers were viewed as influential and inspiring, and based on their actions authentic teachers tended to be recognized as instruments of change. These results were shared in an interactive workshop at STHLE 2022, which discussed how educational leadership is currently framed in higher education, and guided participants in self-reflection as educators and leaders to formulate calls to action involving educational leadership and authentic teaching.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".