<i>Forest Grove v. T.A</i> . Rejoinder to Zirkel
Bibliographic record
Abstract
In this issue, Professor Perry Zirkel argues that the points presented in the Dixon, Eusebio, Turton, Wright, and Hale treatise of the Forest Grove School District v. T.A. Supreme Court case confuses “legal requirements with professional norms.” Although we appreciate Zirkel’s acknowledgment that our position reflects the professional norm—that comprehensive evaluation of psychological processes is critical for identifying children with learning and other disabilities—this position does not equate with “confusion” regarding the Individuals with Disabilities Education Improvement Act statutory or regulatory requirements, or legal precedents, presented in Dixon et al. On the contrary, Dixon et al. specifically address all three requirements in their article, suggesting Zirkel’s rebuttal is not supported. The resultant effects of Zirkel’s arguments on public opinion, professional conduct, and individual children served by the law will be elucidated in this rebuttal.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".