Creating Visionaries Through Positive Leadership: Shifting Educational Paradigms Towards Strengths
Bibliographic record
Abstract
Elements of strength-based pedagogy are evident in current practices being implemented in Canadian schools as well as internationally. Classroom teachers appreciate the importance of creating a positive learning environment for students where the latter feel a sense of belonging, choice, and self-efficacy toward their learning (Deci & Ryan, 2008; Rickabaugh, 2016). While many educators apply such practices at the classroom level, strength-focused pedagogies can be organized through the conceptualization of a unifying framework. Building on research which proposed a dual-dimensional approach to student support services, this article explores the role of school leadership in shifting a school’s culture toward one that values, identifies, and leverages the strengths of students and educators to promote flourishing within their schools. Employing an Appreciative Inquiry action research design (Cooperrider et al., 2000; Stowell, 2012) to engage research participants, this study used Keyes’ (2002) dual-dimensional model as a lens through which to investigate the application of strength-based concepts and practices within school and classroom settings. An Appreciative Inquiry (AI) Action Research Design (Stowell, 2012; Cooperrider et al., 2000) was used to engage research participants, using Keyes’ dual-dimensional model (Keyes, 2002) as a lens through which to investigate the use of strength-based concepts and practices within school and classroom settings.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".