NARRATIVES Radical, Skewed, Benign, and Calculated: Reflections on Teaching Diversity
Bibliographic record
Abstract
This narrative shares my experiences teaching diversity in an undergraduate social work program. First, J begin by sharing my experience teaching diversity as a Ph.D. student. Second, J discuss my experience teaching social work with First Nations in Canada and tell how this experience influenced how I later taught diversity. Third, I attempt to define diversity and discuss how broad and elusive J have found this topic to be. Fourth, I share different instructor roles I assumed in order to get students to appreciate the importance of this course. In this narrative, "Indigenous " and "First Nations " Peoples are used interchangeably to refer to the aboriginal Nations of the United States. I avoid, as much as possible, the terms "Indian, American Indian, and Native American " because I consider them to be colonized identities. When they are used, it is only in the context of a direct quote. by
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.045 | 0.064 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.006 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".