Recognizing the Need for a Trauma-Informed Approach in the Evaluation of an Indigenous Program in British Columbia
Bibliographic record
Abstract
Trauma is prevalent in our society. As culturally responsive evaluators, we often engage with Indigenous peoples to discuss issues that are traumatic. While we have been trained as culturally responsive evaluators to create safe spaces, build relationships, use collaborative and dialogic approaches, and ensure that our methods and methodologies are culturally commensurate with the community’s cultural context, we can still cause harm. If trauma is not addressed and processed appropriately, our work can leave participants with heavy and unprocessed emotions, exemplifying what is essentially an extractive approach inconsistent with the principles of culturally responsive practice. In this practice note, we describe a culturally responsive and Indigenous-focused evaluation we conducted of an Indigenous program housed within a non-Indigenous government organization. Our experience during data collection underscored the need to understand the influence of historical trauma on Indigenous individuals and communities ethically.
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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 | 0.047 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".