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Record W4300198552 · doi:10.25071/1913-5874/37391

Disempathy and emotional witnessing in passport photography

2016· article· en· W4300198552 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInTensions · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropological Studies and Insights
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPoliticsState (computer science)EmpathySocial psychologyPrerogativeIdentity (music)PsychologyInterpersonal communicationSociologyAestheticsPolitical scienceLawComputer scienceArt

Abstract

fetched live from OpenAlex

Passport photography bans the display of emotion and aims at suppressing what speaks to our individual circumstances. This paper argues that the disempathy that results provides a starting point for moral imagination and a method for preserving political engagement beyond the reach of the state. The sharing of emotion concerns political theorists, however, who worry that judgment can be swamped by sentiment, making empathy an unruly or unreliable political resource. Passports represent an aspiration to regulate complex emotions by making identity and belonging a matter of state prerogative rather than interpersonal exchange. The rise of biometrics provides an opportunity to bring emotion under state supervision, dictating its display in a manner that echoes forms of social and racial performance. Efforts to pacify citizens may boomerang, nonetheless, by setting the conditions for unconventional forms of looking and recognition, methods available to individuals but not the states that govern them.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.459

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 head0.032
GPT teacher head0.317
Teacher spread0.285 · 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