The Quadratic Pathways Model to Promote Principals' Self-efficacy and Resilience in Low-socioeconomic Schools in Alberta
Bibliographic record
Abstract
This paper presents the Quadratic Pathways Model (O’Neill, 2013), which emerged from the findings of a doctoral study that investigated the influences on principals’ self-efficacy and resilience in low-socioeconomic-status (SES) schools. This model was developed to promote principals’ efficacy and resilience while exercising leadership agency in these challenging contexts. Principals leading in low-socioeconomic-status (SES) schools experience unique challenges from their counterparts in wealthier contexts. The literature identified that educators cope with students with academic problems, student disengagement, mental health concerns, behavioral issues, and parental disengagement. Additionally, many students lack the basics of life. Principals also contend with many teachers who believe some parents and students do not value education. Consequently, while cognizant of their need to be instructional leaders, some principals believe they cannot fully engage in the instructional aspect of their role due to the many pressures and competing demands on their attention, and the subsequent frustration influences their efficacy. This study employed a theoretical framework founded upon Bandura’s Social Cognitive Theory (1986, 1997), particularly pertaining to self-efficacy, and also integrated resilience research (Patterson & Kelleher, 2005), leadership theories (Leithwood et al., 2004), and contextual literature, which explored the complexities of low socioeconomic status schools. The methodology was mixed methods utilizing questionnaires (n=42) and interviews (n=13) with principals in low-SES schools across the province of Alberta, Canada (Creswell & Guetterman, 2019). Although there were several key themes to emerge from the research, this paper focuses on the synthesis of a new model, the Quadratic Pathways Model, which was designed to inform system leaders of the various components that could enhance and sustain high principal efficacy and resilience in low-SES contexts. It offers principals a template that positively reconceptualises instructional leadership practices that enhance and nurture student success in challenging school contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".