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Record W2175807348 · doi:10.1108/jat-10-2014-0027

TalkBox: a DIY communication board case study

2015· article· en· W2175807348 on OpenAlexaff
Foad Hamidi, Melanie Baljko, Toni Kunić, R. M. Feraday

Bibliographic record

VenueJournal of Assistive Technologies · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsYork University
Fundersnot available
KeywordsOriginalityParticipatory designCitizen journalismAssistive technologyEngineeringDesign technologyValue (mathematics)Design methodsKnowledge managementComputer scienceHuman–computer interactionEngineering managementEngineering ethicsSystems engineeringWorld Wide WebSociologyQualitative researchOperations management

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to present TalkBox, an affordable and open-source communication board for users with communication or speech disorders. Making and tinkering methods are combined with community engagement and participatory design to create a democratic and accessible approach to assistive technology design. Design/methodology/approach – The authors employed a community-engaged participatory design methodology where we incorporated input from stakeholders into the design of the interface. Close collaboration with our community partner allowed us to make informed decisions on different aspects of the design from sourcing of the material to testing the prototype. Findings – Through describing TalkBox, the paper presents a concrete example of how assistive technology can be designed and deployed more democratically, how collaborations between academia and community partners can be established, and how the design reflects different aspects of the methodology used. Originality/value – This paper explores the question of how can open-source technology and making methods contribute to the development of more affordable and inclusive designs through a concrete example.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.173
GPT teacher head0.476
Teacher spread0.303 · 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.

Study designObservational
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

Citations17
Published2015
Admission routes1
Has abstractyes

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