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Record W7070954548

Queering the Rural in Contemporary Argentine Cinema: Taekwondo, Esteros, and Como Una Novia Sin sexo

2017· dissertation· en· W7070954548 on OpenAlexaff

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicTattoo and Body Piercing Complications
Canadian institutionsQueen's University
Fundersnot available
KeywordsQueerNarrativeMovie theaterNormativeRomanceIdentity (music)Rural areaPublic space
DOInot available

Abstract

fetched live from OpenAlex

In queer social histories, the city has been privileged as a normative space for queer identity and social progress to be actualized. Compared with urban spaces, rural spaces continue to be perceived as always dangerous and antagonistic for people who identify as queer. In cinema with queer images, this narrative of the rural as hostile is being challenged by films with queer images that contain positive narratives within a rural or non-urban milieu. In other words, these films are presenting counter-narratives to what scholar Jack Halberstam has called metronormativity. Under a metronormative optic, it is assumed that queer people in a rural space will adopt the spatial narrative of movement from that rural space to an urban space. In 2016, three Argentine films were released that challenged metronormative assumptions: Marco Berger and Martín Farina’s Taekwondo, Papu Curotto’s Esteros, and Lucas Santa Ana’s Como una novia sin sexo / Bromance. Through the representation of queer desire in rural spaces, these films offer a counter-narrative to the privileging of the urban in queer sociality. Through close readings of these three films that take into consideration sexual fluidity, bodily experience, and their intersections with natural elements and iconography, it is evident that there is a contemporary impulse to cinematically convey queer rurality, an understudied and overlooked phenomenon.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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