Bibliographic record
Abstract
In response to concerns as to how to respectfully mobilize Canada's 2015 Truth and Reconciliation (TRC) 94 Calls to Action in our teaching/learning and/or life practices, I developed the Four Protocols of Engagement as a starting point for those ready to authentically engage with First Peoples, their/our lands, and ways of doing, knowing, and valuing. I demonstrate how I apply the Four Protocols in my own work through detailing how each protocol enacted requires preparatory knowledge seeking and actions to make meaningful and impactful Land Acknowledgements. I conclude by reflecting on the content and practices outlined in this example of implementing the Four Protocols of Engagement using the HOW self-assessment tool that I designed for use prior to and during our engagement with knowledges and practices from Nations not our own, in order to ensure that we are approaching this work in authentic, non-appropriating, heartfelt, humble, and mutually respectful and beneficial ways.
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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 | 0.264 | 0.397 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".