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Record W2060600660 · doi:10.1145/1394445.1394472

Sharing digital photographs in the home through physical mementos, souvenirs, and keepsakes

2008· article· en· W2060600660 on OpenAlexafffund
Michael Nunes, Saul Greenberg, Carman Neustaedter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAffordanceComputer scienceWorld Wide WebPublishingMultimediaAdvertisingHuman–computer interactionArtBusiness

Abstract

fetched live from OpenAlex

People now easily share digital photos outside the home via web publishing and gift-giving. Yet within the home, digital photos are hard to access and lack the physical affordances that make sharing easy and opportunistic. To promote in-home photo sharing, we designed Souvenirs, a system that lets people link digital photo sets to physical memorabilia. These mementos trigger memories and serve as social instruments; a person can enrich their story-telling by moving the physical memento close to their large-format television screen, and the associated photos are immediately displayed. We implemented Souvenirs, and then re-examined our design premises through contextual interviews with 20 households. Families described their current practices of photo sharing and memento use, and also reacted to the Souvenirs design. Based on these interviews, we redesigned Souvenirs to better fit the real practices of photo and memento use in the home.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.264
Teacher spread0.236 · 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

Citations64
Published2008
Admission routes2
Has abstractyes

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