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Record W3166195276 · doi:10.32920/24073371

Alimentos, prácticas alimentarias y experiencia de la inmigración

2023· preprint· es· W3166195276 on OpenAlexaffabout
Mustafa Koç, Jennifer M. Welsh

Bibliographic record

Venuenot available
Typepreprint
Languagees
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHumanitiesArtSociologyPolitical science

Abstract

fetched live from OpenAlex

<p>La comida es más que una fuente básica de nutrientes; es también un componente clave de nuestra cultura, central en nuestro sentido de identidad. Las identidades, sin embargo, no son construcciones sociales fijas, sino que se construyen y reconstruyen dentro de ciertas formaciones sociales reflejando los constreñimientos estructurales reales e imaginados y las experiencias de vida de los sujetos. Este artículo examina las relaciones dinámicas entre la comida, la identidad social y la experiencia de los/as1 inmigrantes. Como un período espacial y culturalmente transicional, el proceso inmigratorio introduce posibilidades de cambio así como de resistencia a los nuevos hábitos, nuevos comportamientos y nuevas experiencias culturales. Estos cambios, a la vez, afectan nuestra salud física y mental, nuestras auto-percepciones, y nuestras relaciones con los otros. Este artículo ofrece algunas apreciaciones analíticas sobre esta transición cultural, y su impacto sobre los constreñimientos sociales de la seguridad alimentaria entre un grupo de inmigrantes en Toronto, de modo tal de poder evaluar la compleja dinámica de la reconstrucción identitaria. Se plantea que tanto la política de igualdad como la política de reconocimiento son relevantes en la seguridad alimentaria de los/as inmigrantes. </p>

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.041
GPT teacher head0.305
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2023
Admission routes2
Has abstractyes

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