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Record W3086653650 · doi:10.7202/1070874ar

Sensing the Border at Roxham Road

2020· article· en· W3086653650 on OpenAlexvenueaboutno aff
Gwendolyne Cressman

Bibliographic record

VenueIntermédialités Histoire et théorie des arts des lettres et des techniques · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)SilhouetteDimension (graph theory)Indeterminacy (philosophy)ConfusionBorder crossingLayeringSociologyVisual artsPolitical scienceArtLawComputer scienceEpistemologyPsychologyArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Roxham, a creation by Canadian photographer Michel Huneault, produced by the National Film Board (NFB), is a virtual reality project that gathers a series of 33 photographs documenting 180 irregular migrant border-crossing attempts between February and August 2017 at Roxham Road, on the Canada-US border. In order to preserve the identities of the border-crossers, the photographer shows the migrant figures in silhouette, their bodies collaged in composite images of textiles taken by Huneault during the 2015 migrant crisis in Europe. The palimpsestic layering of fabrics and voices on a three-dimensional map, which the virtual reality device allows, underscores the confusion at the border. The ontological and epistemological indeterminacy that results puts into question the representation of the border. With its emphasis on the visual, the aural, the sense of touch as well as its interactive dimension, Roxham seeks to make the experience of human beings at the border more authentic and more real, while underscoring its fundamental opacity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.055
GPT teacher head0.292
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2020
Admission routes2
Has abstractyes

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