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Record W3008990907 · doi:10.1386/nl_00012_1

Universality, spectrality, proximity: The cinematic faces of Europe's other

2020· article· en· W3008990907 on OpenAlexaff
Boris Pantev

Bibliographic record

VenueNorthern Lights Film and Media Studies Yearbook · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy, Ethics, and Existentialism
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsUniversality (dynamical systems)PoliticsSociologyNorm (philosophy)HospitalityHuman rightsConstitutionPosthumanLaw and economicsEpistemologyLawPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract For a number of scholars, the crisis of European identity is a result of the asymmetry between Europe's universal normative claims and its particular ethnic, cultural and racial contexts. This asymmetry is epitomized by the EU's failure to ratify its legally binding constitution. For others, the same asymmetry constitutes the very condition of being a European. Those envision Europeanness beyond its regulative idea as the ethical injunction of a promise to 'the other'. This article probes the validity of each of these arguments by juxtaposing two groups of films. Gianfraco Rosi's Fire at Sea (2016) and Ai Weiwei's Human Flow (2017) remain committed to the idea of human rights as a universal norm, an idea whose most prominent advocate is Jürgen Habermas. As an alternative to this view, Guido Hendrikx's Stranger in Paradise (2016) and Christian Petzold's Transit (2018) are taken to demonstrate what Jacques Derrida describes as 'hospitality' and 'spectrality'. Based on this analysis, the paper finds both models inadequate to hold together the irreconcilable modalities of the political and the ethical . To address this deficiency, it revisits Levinas' account of 'justice beyond the face' to propose an extension of the ethical view of Europeanness into the spheres of international law and public institutions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.397

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 studies0.0000.001
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 head0.105
GPT teacher head0.268
Teacher spread0.163 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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