MétaCan
Menu
Back to cohort
Record W2943136941 · doi:10.1145/3290607.3313272

PhotoFlow in Action

2019· article· en· W2943136941 on OpenAlexafffund
Benett Axtell, Cosmin Munteanu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoGovernment of Canada
KeywordsAction (physics)Computer sciencePhysics

Abstract

fetched live from OpenAlex

Family connections are maintained through sharing reminiscences, often supported by family photographs which easily prompt memories. This is increasingly important as we age, as picture-based reminiscence has been shown to reduce older adults' social isolation. However, there is a gap between sharing memories from physical pictures and the limited support for oral social reminiscence afforded by digital tools. PhotoFlow supports older adults' picture-mediated social storytelling of family memories using an intuitive metaphor mirroring sharing physical family pictures on a table top. The app uses the speech of oral storytelling to automatically organize pictures based only on what has been said. This simplifies the overall process of family picture interactions by leveraging one enjoyable aspect to ease a more effortful one. In particular, the familiar table top interaction metaphor has the potential to bridge the gap between physical picture reminiscence and managing digital picture collections.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score0.508

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.6440.216

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 head0.022
GPT teacher head0.315
Teacher spread0.293 · 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.

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

Citations10
Published2019
Admission routes2
Has abstractyes

Explore more

Same topicTechnology Use by Older AdultsFrench-language works237,207