Applying First Nations Holistic Lifelong Learning to the Study of Crime
Bibliographic record
Abstract
Since the 1970s, critics have asked universities to “do more” to support Indigenous learners and learning. Universities usually respond by increasing Indigenous student and faculty representation on campuses and adding on units with Indigenous content in existing courses. However, a lot of curriculum and pedagogy remains vacant of Indigenous understandings of learning and perspectives on higher education content and topics for discussion. This paper applies epistemological lessons in the First Nations Holistic Lifelong Learning Model (2007) to the study of crime in America. Its inspiration comes from a guest lecture delivered by myself in an introductory sociology class. The students who take this class are registered in professional programs at a large private university in Rhode Island, United States. I describe the class’s context and use of the model with students in an engaged inquiry format to talk about the subject of the day: crime. This discussion can help faculty consider promising practices for grounding course content in Indigenous epistemologies. Keywords: Indigenous epistemologies; crime; higher education
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".