Finding Fitting Solutions to Assessment of Indigenous Young Children’s Learning and Development: Do It in a Good Way
Bibliographic record
Abstract
Standardized, norm-referenced assessments of young children’s learning and development pose a number of challenges when used with Indigenous children, beginning with the very notion of the construct “early childhood” that runs counter to some Indigenous ways of knowing and being. Indigenous community leaders and knowledge keepers reject the idea that all children should develop according to a homogenizing universal standard that is not grounded in specific culturally based goals and practices surrounding children’s development and does not respect each child’s unique character. Three key problems arise with creating appropriate assessment of Indigenous young children’s learning and development: 1) assessment in early childhood programs is often done from the perspective of whether children are on track to be ready for school; 2) school systems, early childhood programs, and practitioners face a barrage of pressure to measure children’s “progress” against universalist norms derived from Euro-Western ways of knowing and goals for children’s development; and 3) knowledge of diverse Indigenous young children’s varied lived experiences in today’s urban and rural communities is extremely limited. This paper discusses these obstacles and draws from the author’s many years of collaborating with Indigenous children, families, and communities to co-create culturally relevant assessment in a good way.
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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 | 0.063 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".