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Record W4383652782 · doi:10.1145/3563703.3593068

Beyond Looking Back: Designing Interactive Technology Together to Support Blind People's Experience of Reminiscence

2023· article· en· W4383652782 on OpenAlexafffund
MinYoung Yoo

Bibliographic record

VenueDesigning Interactive Systems Conference · 2023
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsReminiscenceParticipatory designBlindnessPsychologyReciprocity (cultural anthropology)CreativityCitizen journalismUser experience designComputer scienceHuman–computer interactionSocial psychologyCognitive psychologyEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

There is growing attention in the HCI community on how technology could be designed to enrich experiences of reminiscence on past life experiences. Yet, this research has largely overlooked people with blindness. My doctoral research is oriented toward understanding and supporting blind people's preferences, wishes, dreams, desires and tensions around the experience of reminiscence. I plan to explore research goals by designing and creating an interactive system through a participatory design and co-speculation approach. The research prototype can be lived with blind people in their homes to support their experience of reminiscence. With reciprocity in mind, I aim to involve participants in exploring, designing and reflecting together in all stages of the proposed research. The initial work of understanding blind people's experiences of reminiscence is presented, and how these insights shape the next steps, along with the key values we seek to unpack in the later stages, are described.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0060.007
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.046
GPT teacher head0.324
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes2
Has abstractyes

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