Bibliographic record
Abstract
The purpose of this article is to review common assessment practices for Indigenous students. We start by presenting positionalities—our personal and professional background identities. Then we explain common terms associated with Indigeneity and Indigenous and Western worldviews. We describe the meaning of document analysis, the chosen qualitative research design, and we explicate the delimitations and limitations of the paper. The review of the literature revealed four main themes. First, assessment is subjugated by a Western worldview. Next, many linguistic assessment practices disadvantage Indigenous students, and language-specific and culture-laden standardized tests are often discriminatory. Last, there is a pervasive focus on cognitive assessment. We discuss how to improve assessment for Indigenous students. For example, school divisions and educators need quality professional development and knowledge about hands-on assessment, multiple intelligences, and Western versus Indigenous assessment inconsistencies. Within the past 20 years, assessment tactics for Indigenous students has remained, more or less, the same. We end with a short discussion addressing this point.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".