Guiding the Practice: Design thinking to meet the needs of FNMI students in Saskatchewan
Bibliographic record
Abstract
Throughout the last 30 years, a concerted effort has been to undo the legacy of First Nations Education in Canada. With this, there has been an increase in the exploration of how to teach effectively and design courses for a FNMI (First Nations, Metis, Inuit) audience. What are the considerations that we should take when designing a course? What specifics must we consider in developing courses for FNMI learners in a school setting? How do we ensure that we meet our learners where they are instead of forcing them to exist in a paradigm that does not work for them? Throughout this process, I have discovered that though we talk quite a bit about this topic, there are many areas that we lack understanding of or a desire to take into account regarding the actual variables that exist within the teaching of FNMI students. This process and these proceedings will act as a starting point for us to develop a guide for many teachers that directly synthesizes the information available within the realm of teaching and learning.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".