A Conversational Approach to Learning About Learning: Embedded Indigenous Evaluation and Communities of Practice
Bibliographic record
Abstract
In the context of Indigenous and decolonial evaluation, who is doing the work matters, the way that we do this work matters, and how stories of this work is shared matters. Based on their experiences as Learning Facilitators, the authors share their insights and questions about how learning might be supported through an embedded learning approach. A conversational approach used to share these learnings is congruent with the ways of working that the authors have established as important within their Community of Practice. Two key areas are addressed. First, we share experiences engaging in a EleV Learning Facilitators Community of Practice including the potential value and opportunity that a Community of Practice can offer when working within an Indigenous evaluation and learning framework. Second, we share experiences, insights, learning, and questions about the role, purpose, and opportunities in embedding Learning Facilitators in initiatives that focus on systems change.
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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 | 0.064 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.055 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.007 | 0.009 |
| 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".