What is at stake in knowing the content and capabilities of children’s minds?
Bibliographic record
Abstract
Many significant changes in perspective have to take place before efforts to learn the content and capabilities of children’s minds can hold much sway in educational testing. The language of testing, especially of high stakes testing, remains firmly in the realm of ‘behaviors’, ‘performance’ and ‘competency’ defined in terms of behaviors, test items, or observations. What is on children’s minds is not taken into account as integral to the test design and interpretation process. The point of this article is to argue that behaviorist-based validation models are ill-founded, and to recommend basing tests on cognitive models that theorize the content and capabilities of children’s minds in terms of such features as meta-cognition, reasoning strategies, and principles of sound thinking. This approach is the one most likely to yield the construct validity for tests long endorsed by many testing theorists. The implications of adopting a cognitive basis for testing that might be upsetting to many current practices are explored.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.040 |
| Scholarly communication | 0.009 | 0.034 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".