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Record W2807224895 · doi:10.1080/08865655.2016.1222875

An Alternative Border Metaphor: On Rhizomes and Disciplinary Boundaries

2018· article· en· W2807224895 on OpenAlexvenueno aff
Caleb Bailey

Bibliographic record

VenueJournal of Borderlands Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMetaphorHegemonyNexus (standard)DisciplineSociologyIdeologyPolitical sciencePoliticsLawSocial scienceLinguisticsEngineering

Abstract

fetched live from OpenAlex

The border is an inherently transnational nexus of cultures and identities yet, when approached and articulated from within the discipline of American Studies, often results in a re-entrenchment of singular national histories, cultures, and identities. This essay seeks to address the propensity of the discipline to remain sited within and focused upon the nation-state of the United States of America and its hegemonic ideology at the expense of more wide-ranging hemispheric analyses that account for the fluid, transnational, and borderless identities that America—in its continental configuration—has always been home to. Invoking Deleuze and Guattari’s critical metaphor—the rhizome—the paper develops and deploys analytical techniques which seek out and highlight connections and alternative configurations of existing material, often obfuscated by the supposed territorial integrity of nation and its inhabitant’s identities. Two key texts (Laurie Ricou’s The Arbutus/Madrone Files (2002) and Guillermo Verdecchia’s Fronteras Americanas (1993)) are offered as examples of the ways in which positioning the border itself as a possible rhizomatic line of flight can ensure that borderlands cultural productions retain the multiplicity of the identities that they enact as both spatially and temporally (in)distinct and as challenges to the perpetuation of the border as a static and dichotomous entity.

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 score0.405
Threshold uncertainty score0.999

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 studies0.0020.003
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 head0.037
GPT teacher head0.402
Teacher spread0.365 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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