Complex Poverty and Urban School Systems: Critically Informed Perspectives on the Superintendency
Bibliographic record
Abstract
Complex Indigenous and racialized poverty exists in Canada. Child poverty obviously has a negative impact on our youth who are served by school systems. As Silver (2014, 2016) and others have demonstrated, poverty can lead to poor educational outcomes. The purpose of this study was to examination the understandings and actions of four superintendents in Winnipeg, Manitoba related to complex Indigenous and racialized poverty. The superintendency is incredibly complex and extremely political, and there cannot be a recipe book from which superintendents can help advance the cause of greater equity for all our students. That said, we can learn from the stories of those who have made a difference, no matter how small or contextualized. We can advance our knowledge to inform how superintendents can contribute to the creation of educational environments in which people challenge, develop, and, in the words of Foster (1986), “liberate human souls” (p. 18). Using a qualitative approach informed by critical theory, this study explores how the superintendents understood issues related to complex, racialized poverty in particular; and how these understandings influenced their work in highly complex, political, and contextual work environments. In this study, each of the superintendents participated in a series of individual interviews and a group dialogue. The study attempts to ascertain (a) what the participants believed about complex poverty and how they have come to these understandings, (b) how they described the socio-political and organizational environments that informed and influenced their work as superintendents and; what they were able and unable do to mitigate the effects of poverty upon students and their communities, and (c) what actions have they undertaken to attempt to address issues of racialized poverty and what else they think should be done in schools, in school systems, and in the greater communities.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.059 | 0.079 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".