MétaCan
Menu
Back to cohort
Record W7000228208

¡están en nosotros! el horror de la marginalidad y las fantasías de la clase media en un estudio de cine y literatura en Argentina, México y El Salvador durante la época neoliberal

2022· article· en· W7000228208 on OpenAlexaboutno aff

Bibliographic record

VenueScholarlyCommons (University of Pennsylvania) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansIdeologyNeoliberalism (international relations)Reading (process)Order (exchange)The ImaginaryMiddle classQuarter (Canadian coin)Agency (philosophy)Class (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

The present dissertation analyses several works of film and literature from the last quarter of a century in order to explore the influence of neoliberalism in Latin America, with a focus on Argentina, Mexico and El Salvador. The hypothesis is that the changes promoted by this program have been so deep, that have become an inescapable topic within these texts. Furthermore, this dissertation examines the constant presence of the marginal masses in these works, and their immediate relationship with the reconfiguration of neoliberal society. Through a close reading of the texts under a cultural studies optic, this research confirms the richness of fantasy, violence and science fiction for an analysis of the Latin American middle class’ ideology. Moreover, the masses appear as an integral part of such ideology and its psychological consequences. We can then observe two main tendencies regarding the vision of said masses: one as a polluting element that risks corrupting the lives of the middle class (Argentina), and one that identifies them with the sunk or the loser, as those who were unable to adapt to the new times and whose death has been naturalized (Mexico, El Salvador).

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
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 head0.005
GPT teacher head0.263
Teacher spread0.258 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScholarlyCommons (University of Pennsylvania)Same topicLatin American Literature StudiesFrench-language works237,207