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Record W4403765374 · doi:10.3828/jlcds.2024.37

“When Father Christmas Is the Gaslighter”

2024· article· en· W4403765374 on OpenAlexaff
Katherine Runswick‐Cole, Patty Douglas, Penny Fogg, Sarah Alexander, Stephanie Ehret, Jen Eves, Barbara Shapley-King, Martha Ward, I. Moreton Wood

Bibliographic record

VenueJournal of Literary & Cultural Disability Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsTrent UniversityQueen's University
Fundersnot available
KeywordsChristmas treeHistoryArtGenealogyArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.131
GPT teacher head0.342
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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