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Record W7019860599

Implementation of Anti-Racist, Anti-Oppressive Strategic Plans and Policies in School Divisions: An Organizational Improvement Plan

2024· dissertation· en· W7019860599 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)Strategic planningIndigenousPlan (archaeology)IdeologyQualitative researchOrganizational change
DOInot available

Abstract

fetched live from OpenAlex

Across Canada, many school divisions’ recent and emerging strategic plans are equity-focused and marked by the discourses of anti-racism and anti-oppression. These documents identify the systemic practices as the root cause of the persistent educational inequities between Indigenous and non-Indigenous learners and among intersectionally dominant and marginalized students. These practices issue from and adhere to the systemic logic(s) of intergenerational settler colonialism in Canada. Typically, while these documents clearly articulate core principles and end goals, they leave the “how” – the corresponding procedural tools of strategic plan actualization and policy implementation -- up to systems and system actors. \nMany school divisions have chosen an ideological implementation approach (Matland, 1995) which begins with immersing school-based and division-office administrators in anti-racist, anti-oppressive professional learning. While qualitative data reveals that this approach yields positive results for the first two stages of Thomas Guskey’s model (2008) for evaluating professional development, it does not (yet) extend to identify specific division and school-based structures, processes, and collective efficacies which will reliably produce the policies’ intended effects in the lives of diversly diverse student bodies. \nTwo significant barriers to anti-racist policy implementation have been identified: first, the resistance of internal and external stakeholders to a decolonial framing of the issue and its (re)solution; second, the challenge of determining the policy structures and instruments required to effect the systemic change these documents describe.\nThe methodology the researcher chose to investigate and respond to this problem of practice is an Organizational Improvement Plan (OIP). The purpose of this research was to develop an OIP which mitigates the barriers and forms of resistance common to ideological implementation efforts and provides procedural tools to meet the specific demands of decolonial organizational change. \nThe result is twofold: the System of Tools for Equitable Schools, grounded in the principles and practices of culturally sustaining, justice-oriented pedagogy, and a three-year Organizational Improvement Plan for schools and school divisions, grounded in the critical action research paradigm and consistent with adaptive and distributed models of educational leadership. The OIP’s theory of action positions educators’ collective practices, not their beliefs, as the starting point of decolonial organizational change, and, furthermore, posits that changing patterns of inequity at the classroom level is the key to “closing the gaps” at the school and division levels. \nThe plan describes how the entire staff of educators and administrators will engage in iterative cycles of critical action research as they implement The System of Tools for Equitable Schools over a three year period. The plan can be implemented in a single school, a group of schools, or an entire division. The goal of the research and intervention design in this dissertation is to evaluate the potential of a process approach (Matland, 1995) to accelerate systemic change in decolonizing school divisions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.960

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0220.009
Scholarly communication0.0130.003
Open science0.0050.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.245
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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