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Record W3205717149 · doi:10.1017/s1744552321000380

Stigmatisation, identities and the law: Asian and comparative perspectives

2021· article· en· W3205717149 on OpenAlexaboutno aff
Lynette J. Chua, George Baylon Radics

Bibliographic record

VenueInternational Journal of Law in Context · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsContent (measure theory)Action (physics)Content analysisPolitical scienceLawSociologySocial scienceMathematics

Abstract

fetched live from OpenAlex

Impressive growth in Asia, as one of most dynamic regions in the world, sometimes happens at the expense of marginalisation.Individuals who do not fit normative ideals, who are deemed economically unproductive or who do not participate in heterosex-centred reproduction are often regarded as different, even deviant, and come to take on or are given identities that are marginalised.These include, but are not limited to, people with physical or intellectual disabilities, the elderly, gender and sexual minorities, never-married parents or unmarried people.Stigmatisation can be pronounced in homogenous or insular societies and communities that use 'culture' and 'tradition' as a justification to extract conformity.It can also appear where the self-sufficiency of individuals and heterosexual, biological and nuclear families is touted as a moral virtue that aligns with neoliberal and anti-welfare ideologies.Against this backdrop, we sought papers that would speak to the theme of this Special Issue.We were interested in the processes of stigmatisation involving a range of interactions and relationships, including being treated as burdensome and unproductive members of society, or regarded as a threat to the social order, as well as social processes in which those who are stigmatised respond to such treatment by coming up with strategies, taking action or deciding not to take action.How do these processes emerge and transform, and what do they look like?How do people respond to differential treatment based on their stigmatised identities at home, at work or against state authorities?How are they protected or persecuted under the law and what forms of recourse do they have?What do these experiences tell us about the manner in which law matters to identities, human relationships and social life?In collaboration with David Engel (SUNY Buffalo, Law), Rosie Harding (University of Birmingham, Law) and Sida Liu (University of Toronto, Sociology and Law), we first put out a call for workshop papers.We received forty submissions, out of which we chose fifteen.Although we had planned to hold the workshop in person in June 2020 at the National University of Singapore, we converted the workshop to an online event due to the coronavirus disease (Covid-19) outbreak.At the workshop, the authors received feedback from an online audience around the world, engaged in conversation with one another and received feedback from us and our three collaborators.In the end, six of the authors moved forward with the Special Issue with this Journal.Together, these authors cover a wide range of stigmatised identities: from the more conventional 'blemishes of character' attributes as described by Goffman, such as sex workers, sexual minorities and ethnic minorities, to people whose identities push the boundaries of how we conceptualise stigma, such as elderly prisoners and female international arbitrators.Although our reference to 'stigmatisation' is inspired by Erving Goffman, we did not require the authors to draw extensively from Goffman's Stigma: Notes on the Management of Spoiled Identity (1963) or subsequent scholarship that built on his work in any particular manner.Nevertheless, their analyses of stigma and the processes of stigmatisation help to bring light to the plight of marginalised identities in different parts of Asia and advance the scholarship on stigma and stigmatisation in the Global South.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.349
Teacher spread0.316 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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