Choices, challenges, and constraints: a pragmatic examination of the limits of mental age matching in empirical research
Bibliographic record
Abstract
The work of Ed Zigler spans decades of research all singularly dedicated to using science to improve the lives of children facing different challenges. The focus of this article is on one of Zigler's numerous lines of work: advocating for the practice of mental age (MA) matching in empirical research, wherein groups of individuals are matched on the basis of developmental level, rather than chronological age. While MA matching practices represented a paradigm shift that provided the seeds from which the developmental approach to developmental disability sprouted, it is not without its own limits. Here, we examine and test the underlying assumption of linearity inherent in MA matching using three commonly used IQ measures. Results provide practical constraints of using MA matching, a solution which we hope refines future clinical and empirical practices, furthering Zigler's legacy of continued commitment to compassionate, meaningful, and rigorous science in the service of children.
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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 | 0.383 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.018 | 0.177 |
| Scholarly communication | 0.027 | 0.049 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".