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Record W4415256869 · doi:10.1145/3757584

Double Incomes, Single Calendar: Reimagining Shared Scheduling for Modern Families

2025· article· en· W4415256869 on OpenAlexaff
Zhao Zhao

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2025
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNegotiationOnboardingThematic analysisParticipatory designScheduling (production processes)Intervention (counseling)Citizen journalismConfidentiality

Abstract

fetched live from OpenAlex

Coordinating schedules in double-income households is an ongoing challenge, as traditional calendar tools often fail to meet the needs of families juggling professional and domestic responsibilities. This mixed-methods study investigates the barriers to adopting shared digital calendars and explores how guided onboarding and participatory design can improve their effectiveness for family use. We conducted surveys with 277 participants, interviews with 40 individuals, a one-month calendar adoption intervention with 30 couples, and a design workshop with 12 couples. Our findings reveal persistent obstacles-including privacy concerns, uneven engagement, and limited customization-as well as the potential of targeted support and user-driven design to overcome them. Participants expressed a desire for more flexible privacy controls, role-sensitive features, and smarter coordination support. Thematic analysis across all phases highlights how families negotiate boundaries, share invisible scheduling labor, and creatively adapt tools to fit their routines. We conclude with implications for designing family-centric coordination technologies that balance structure, agency, and trust in everyday scheduling.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.313
Teacher spread0.263 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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