"When Father Christmas Is the Gaslighter": How Special Education Systems Make (M)others "Mad"
Bibliographic record
Abstract
The article is written with, by, and for (m)others whose children have been labelled as having “special educational needs” (SEN). The term (m)others is used to pay attention to the continued impact of the gendered nature of care for (disabled) children at the level of the individual, but also to recognize gender as a social construct, and the many ways of being a (m)other (Anderson). The broad aim is to explore the ways in which special education systems across the global North construct (m)others of disabled children as “mad.” This discussion is timely given the high levels of conflict between parents/carers and global North special education systems in contemporary times. The article explores “madness” as a mechanism of social control produced in special education systems by paying close attention to “intimate encounters” between (m)others and practitioners that occur day-to-day in (special) education settings. The developing analysis is shaped by the concept of “gaslighting,” which offers a useful framework both for understanding deeply affecting and effecting “intimate encounters” between (m)others and practitioners, and for exposing the operations of power in special education systems. The conclusion reflects on what new understandings of (m)others’ madness have been revealed and how they have the potential to (re)shape practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".