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Record W4402796764 · doi:10.1111/aman.28017

An ethnography of joy: Entrepreneurship among Latinx communities in East Los Angeles

2024· article· en· W4402796764 on OpenAlexafffund
Yana Stainova

Bibliographic record

VenueAmerican Anthropologist · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnographyEntrepreneurshipSociologyAnthropologyGerontologyGender studiesPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract How do people living at the intersection of various forms of injury seek out collective experiences of joy? I explore this question through fieldwork with Latinx female and queer artists and entrepreneurs, some of them undocumented, who consciously seek out and enact joy in their communities in East Los Angeles. At the same time, these communities face gentrification, racism, and discrimination. I rest on joy as a conceptual framework that arises out of the analysis and theorizing of my interlocutors, who choose to push back against mainstream representations of their communities as exclusively defined by their suffering. This approach, or what I call “an ethnography of joy,” draws our attention to what joy does in a particular context, how it becomes politically meaningful, and how it intersects and interacts with other phenomena. For example, in this article, I explore the more capacious idea of joy through a particular angle that emerged in my ethnographic research: entrepreneurship, or small business ownership. A focus on entrepreneurship allows me to explore how my interlocutors summon the forces of neoliberalism to seek social mobility, belonging, and community activism.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.040
Scholarly communication0.0000.000
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 head0.050
GPT teacher head0.362
Teacher spread0.312 · 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 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
Published2024
Admission routes2
Has abstractyes

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