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Record W2226735801 · doi:10.3390/soc6010002

Spatial Explorations and Digital Traces: Experiences of Legal Blindness through Filmmaking

2016· article· en· W2226735801 on OpenAlexaffabout
Adolfo Ruiz, Megan Strickfaden

Bibliographic record

VenueSocieties · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBlindnessNarrativeContext (archaeology)PerceptionArchitectureSociologyPsychologyVisual artsPublic relationsPolitical scienceHistoryArtMedicineOptometryLiterature

Abstract

fetched live from OpenAlex

Descriptions of legal blindness, as lived experience—involving continual movement between the world of sightedness and blindness—are largely absent within medical models of disability. In an effort to challenge depictions of blindness as pathology, researchers in this project worked with participants who are legally blind, in a co-designed exploration of built spaces in the city of Edmonton, Canada. In this article we describe a collaborative research method through which participants shared stories while recording their movement through a shopping mall, an art gallery, and a gym. Through this project, participants often took the lead, determining the content and context of urban journeys. Stories and images shared through this collaboration suggest that legal blindness is an alternative way of knowing the world, with unique perceptual experiences, navigational strategies, and complexity that is often unacknowledged within a medically constructed blindness/sightedness binary. In describing the complex relationship between participants, researchers, architecture, and technology we will combine narrative forms of writing with actor-network theory. The sharing of stories, along with lived experiences has led to a project that revolves around ability, as opposed to disability. A link to the film is provided at the end of this article.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.579

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 studies0.0010.002
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.308
Teacher spread0.266 · 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 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

Citations4
Published2016
Admission routes2
Has abstractyes

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