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Record W4297337558 · doi:10.17351/ests2022.961

Brain-Computer Interfaces, Inclusive Innovation, and the Promise of Restoration: A Mixed-Methods Study with Rehabilitation Professionals

2022· article· en· W4297337558 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEngaging Science Technology and Society · 2022
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsMcGill UniversityMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsDeliberationEnthusiasmTechnocracyRehabilitationBrain–computer interfaceEngineering ethicsPublic relationsPsychologySociologyPolitical scienceEngineeringSocial psychologyPsychiatryPolitics

Abstract

fetched live from OpenAlex

Over the last two decades, researchers have promised “neuroprosthetics” for use in physical rehabilitation and to treat patients with paralysis. Fulfilling this promise is not merely a technical challenge but is accompanied by consequential practical, ethical, and social implications that warrant sociological investigation and careful deliberation. In response, this paper explores how rehabilitation professionals evaluate the development and application of BCIs. It thereby also asks how the BCIs come to be seen as desirable or not, and implicitly, what types of persons, rights, and responsibilities are assumed in this discourse. To this end, we conducted a web-based survey (N=135) and follow-up interviews (N=15) with Canadian professionals in physical therapy, occupational therapy, and speech-language pathology. We find that rehabilitation professionals, like other publics, express hope and enthusiasm regarding the use of BCIs for assistive purposes. They envision BCI devices as powerful means to reintegrate patients and disabled people into social life but also express practical and ethical reservations about the technology, positioning themselves as uniquely qualified to inform responsible BCI design and implementation. These results further illustrate the nascent “co-production” of neural technologies and social order. More immediately, they also pose a serious challenge for implementing frameworks of responsible innovation; merely prescribing more inclusive technology development may not counteract technocratic processes and widely held ableist views about the need to augment certain bodies using technology.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativehigh
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.339
Teacher spread0.326 · 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