Beyond the Monster's Wanting and Doing: Special Education as a Barrier and Diacritical Hermeneutics as Possibility.
Bibliographic record
Abstract
This hermeneutic, interpretive case study reflects on an experience with the placement of a student in a specialized classroom who did not want to be there and had informed educators around her of this prior to her placement. She claimed she would “do anything to get kicked out of the placement” and ultimately, this happened. Through this case study I argue that Special Education policy and its infusion into psychology, especially through the Diagnostic and Statistical Manual, conceals or limits the possible ways for such students to be because of how we use that language to frame them. Special Education diagnosis and coding are more than labels, I suggest: they are constitutive and we play a role in that constituting action, I argue. Richard Kearney’s Diacritical Hermeneutic approach reveals possibilities for seeing differences outside of the binary of normal / abnormal. Such an approach could allow us to value such students outside the exteriority of Special Education’s framing. We may more openly see their rights as human beings, thus allowing them the space to tell their stories so that we hear them. Concurrently, I suggest we might also critically reflect on our roles in supporting students. Key words: Discourse, psychology, mental health, special education, subject, democracy, phronesis, experience, pedagogy
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.097 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".