Principals' Work Experiences in Schools Serving Low-Income Households in Urban Ontario
Bibliographic record
Abstract
This study investigated the work experiences of principals in schools that serve students from low-income households in an Ontario urban community. Several research studies have reported that principals experience work intensification, burnout, and stress because of the complexity and changing nature of their work. However, limited studies examined the work of principals in urban low-income communities in Ontario. Given the changing student population and increasing number of students and families living in low-income households after the pandemic, it is necessary to investigate the work demands of principals in schools that serve low-income households. Using a qualitative and interpretive approach, this study examined the work of 11 principals in elementary and secondary schools that serve low-income households in one Ontario urban community. Four focus group sessions, and 7 semi-structured interviews presented rich data to explore the research phenomenon. This study uncovered the composition of students in schools that serve a high population of low-income households, the low-income Canadian-born families and newcomer families. The job of principals in these schools is dominated by complex and voluminous work and time demand, with no downtime and limited resources to support diverse students' needs. These challenges became intensified because of the pandemic, leading to increased work intensification experienced by these principals, which influenced their effort to maximally support learning instruction. The job demands constituted equity issues among the principal workforce, as principalships in these schools were revealed to be different. The findings have significance for policy and practice in Ontario, and future research on areas principals in urban low-income communities can be better supported to promote equitable education for children living in poverty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".