“Membership Has Its Privileges”: The Elusive and Influential Nature of Special Education Expertise
Bibliographic record
Abstract
Special education is brimming with individuals who are often called experts. Speech-language pathologists, consultants, psychologists and a host of others are all, in varying contexts, considered to be experts. But are parents also not experts? And what exactly is an expert? These are all important questions. If perspectives of expertise influence the way in which voices are heard in special education processes, then questions about expert identity and the purported objectivity of expert knowledge and language are ones we need to ponder. In this discussion paper, I draw from two composite narratives – fictionalized accounts of my own experiences – to identify aspects of special education knowledge and language expertise, and consider how they might influence parental inclusion. Given the effects of various forms of expertise in special education, we ought to encourage a form of reciprocity that facilitates parental inclusion. Questions are posed throughout the paper to highlight not only the importance of inclusion and reciprocity, but also ways in which they might be fostered.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".