A Networked Approach for Curricula Implementation in Support of Inclusive Education Reform
Bibliographic record
Abstract
The Maritime Province Department of Education (MPDOE; a pseudonym) has long struggled to address issues of systemic racism that have significantly impacted the academic success and well-being of Indigenous and African Canadian students. When the MPDOE embarked upon its recent inclusive education reform journey, it did so through a series of studies that resulted in a comprehensive reform initiative to address the learning needs of historically marginalized and racialized students. The main thrust of the reform goals focused on curricula to support culturally responsive approaches to learning and the reconfiguring of governance to a more responsive, networked model. Though there is consensus regarding the need for network governance to advance the reform goal of curricula implementation, the legacy of past practices and problematic relationships among the MPDOE, school regions, and historically marginalized communities must be addressed to move forward with this change. The Organizational Improvement Plan (OIP) explores a solution to this problem of practice (PoP) that proposes a focus on social justice-oriented networked leadership models and intergroup and social learning processes for the development of a network team capable of actioning the curricula implementation reform goal. Explored through a reconceptualized critical paradigm that centres Indigenous and African Canadian perspectives, the development of critical consciousness for network team members to action change further undergirds the solution. As a change facilitator and leader at the MPDOE, the support for the development of a network team tasked with planning curricula implementation will be explored through inclusive, distributed, and systems leadership approaches to guide a reimagining of workplace culture and learning at the MPDOE.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".