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Record W4388728994 · doi:10.1080/17503280.2023.2270957

Making room for empathy in contemporary virtual reality cinema

2023· article· en· W4388728994 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueStudies in Documentary Film · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMovie theaterEmpathyVirtual realityAestheticsArtVisual artsMixed realitySociologyMedia studiesPsychologyComputer scienceSocial psychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

This article seeks to redeem the idea that virtual reality (VR) might serve to foster empathy by rethinking both the notion of empathy and the ways contemporary VR films have attempted to generate it. Specifically, this paper considers two forms of embodiment—being and being with—in order to highlight some of the shortcomings of the ‘empathy machine’ discourse. In doing so, I also offer an alternative analogy: a tool for empathy, rather than a machine. Finally, an analysis of two key VR productions that invite users to be with characters—rather than putting users ‘in the shoes’ of a character—allows me to demonstrate how a more productive path towards empathy in VR might instead lay in experiences that create space—or ‘make room’—for the work of empathy to be undertaken by individuals who know and want to use VR as a tool, rather than as an end in itself.

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)0.001
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 score0.213
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.219
GPT teacher head0.373
Teacher spread0.154 · 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