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Record W2523976507

“More Normal Than Welfare”: The Mincome Experiment, Stigma, and Community Experience

2016· article· en· W2523976507 on OpenAlexaboutno aff
David Calnitsky

Bibliographic record

Venue28th Annual Meeting · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStigma (botany)PaymentFraming (construction)WelfareIncome SupportPublic economicsSociologySocial psychologyEconomicsPolitical sciencePsychologyGeographyLaw
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the impact of a social experiment from the 1970s called the Manitoba Basic Annual Income Experiment (Mincome). I examine Mincome's saturation site located in Dauphin, Manitoba, where all town residents were eligible for guaranteed annual income payments for three years. Drawing on archived qualitative participant accounts I show that the design and framing of Mincome led participants to view payments through a pragmatic lens, rather than the moralistic lens through which welfare is viewed. Consistent with prior theory, this paper finds that Mincome participation did not produce social stigma. More broadly, this paper bears on the feasibility of alternative forms of socioeconomic organization through a consideration of the moral aspects of economic policy. The social meaning of Mincome was sufficiently powerful that even participants with particularly negative attitudes toward government assistance felt able to collect Mincome payments without a sense of contradiction. By obscuring the distinctions between the deserving and undeserving poor, universalistic income maintenance programs may weaken social stigmatization and strengthen program sustainability.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.002
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 head0.032
GPT teacher head0.341
Teacher spread0.309 · 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.

Study designQualitative
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

Citations0
Published2016
Admission routes1
Has abstractyes

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