MétaCan
Menu
Back to cohort
Record W4415444830 · doi:10.1145/3663547.3759745

DIY in Action: Understanding Do-It-Yourself Practices from Tangible Symbol Cards

2025· article· W4415444830 on OpenAlexfundno aff
Alexander S.W. Parent, L. Beth Brady, Sarah E. Ivy, Jennifer Hercman, Amy Hurst

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEnthusiasmDisabled peopleSymbol (formal)Augmentative and alternative communicationAssistive technologyBest practice

Abstract

fetched live from OpenAlex

Do-It-Yourself (DIY) assistive technology can be better suited to the needs of people with disabilities than commercial ‘off-the-shelf’ devices. Yet, DIY practices conventionally involve barriers to entry that can dissuade or intimidate practitioners. Tangible symbols (TS) are low-tech augmentative and alternative communication (AAC) products with a history of practitioners working alongside end-users to DIY. In this paper, we study the enthusiasm and practice of DIY with TS. We present results from an initial investigation into DIY practices by practitioners that use TS. We analyze survey data collected by Brady et al. (2025) from 107 respondents, and 73 self-reported customizing their TS products to meet the needs and contexts of the individuals they serve. We share the survey self-reported motivations for engaging in DIY activities. We find that this data offers evidence that there is sustained enthusiasm in DIY with TS by practitioners. We reflect on these findings and draw connections between DIY and assistive technology abandonment, and how commercial products can be designed to support DIY practice. The use of DIY by practitioners signals both a desire to further integrate DIY into their profession, and an interest in future assistive technology products that employ a similar DIY-centered design framework.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.154
GPT teacher head0.404
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicInnovative Human-Technology InteractionFrench-language works237,207