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Record W4408635623 · doi:10.3389/fsoc.2025.1401812

Toward a politics of shame: cripping understandings of affect in disabled people’s encounters with unsolicited advice

2025· article· en· W4408635623 on OpenAlexaffabout
Megan Ingram

Bibliographic record

VenueFrontiers in Sociology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsQueen's University
Fundersnot available
KeywordsShameFeelingSadnessPsychologySocial psychologyEmbarrassmentAffect (linguistics)Qualitative researchResistance (ecology)NarrativeAdvice (programming)SociologyAngerSocial science

Abstract

fetched live from OpenAlex

The prevalence of unsolicited advice in the lives of disabled people is well-catalogued in the mass of articles and social media posts dedicated to the issue. However, less is known about the affective impacts of this advice on disabled people and the potential resistance that may be enacted, such as shame, toward affects labelled negative. The present manuscript builds from original qualitative research to explore the links between emotion, mind, and body that occur in interactions involving unsolicited advice between disabled and non-disabled individuals. Non-probability convenience sampling was used to recruit 15 disabled individuals in Ontario, Canada for participation in semi-structured qualitative interviews that were inductively coded and narratively restoried. Building from these narrative accounts, the research addresses (1) the affective impacts of unsolicited advice on disabled people and (2) how disabled people negotiate the emotional impact resulting from unsolicited advice and blame culture individually and collectively. Ultimately, this research argues that, while unsolicited advice acts as a method of blaming and shaming that has the potential to structure disabled peoples’ lives, disabled people resist feeling ashamed and instead bridge from initial responses of fear and shame toward other emotions such as apathy and sadness in resistant and potentially empowering ways.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.324
Teacher spread0.305 · 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 designObservational
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

Citations1
Published2025
Admission routes2
Has abstractyes

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