Collaborative Professionalism Across Cultures and Contexts: Cases of Professional Learning Networks Enhancing Teaching and Learning in Canada and Colombia
Bibliographic record
Abstract
Abstract Educational inequities that are often systemic and the result of structural oppression persist in schools under/serving minoritized youth and communities. This chapter illustrates how professional learning networks (PLNs) and the practice of collaborative professionalism within them have served to support educators, positioned at multiple levels, in their effort to serve all children well, and especially those who are most marginalized. Collaborative professionalism emphasizes collective responsibility and student and teacher empowerment through PLNs. Further, the collaborative professionalism model incorporates elements of culture and context to ensure that collaborative efforts are responsive to the students and communities educators are purposed to partner with and serve. In this chapter, the authors highlight two such cases of collaborative professionalism through PLNs in Colombia and Ontario, Canada. These cases provide a model for how collaborative professionalism within PLNs can be utilized to enhance teaching and learning for all teachers and students across cultures and contexts, while attending explicitly to educational inequities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".