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Record W4285017810 · doi:10.22215/etd/2022-15111

First-Person Parent: A Longitudinal Study Exploring the Role of Wearable Cameras in Parent-Child Interactions

2022· dissertation· en· W4285017810 on OpenAlexaff
Maryam Sadeghian

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsCarleton University
Fundersnot available
KeywordsWearable computerPsychologyLongitudinal studyWearable technologyHuman–computer interactionQualitative researchDevelopmental psychologyComputer scienceMedicineSociology

Abstract

fetched live from OpenAlex

This study investigates the user experience of wearable cameras as a less disruptive way to capture and share interactions between parents and their children.In this research, I conducted a longitudinal study and 14 parents (12 female and two male), and 27 children participated in a six week to see how a wearable camera integrated into sunglasses affected parent-child interaction in their everyday routines.According to the preliminary findings of a qualitative travelogue research, utilizing a wearable camera or other semi-inconspicuous wearable devices will help parents to document moments between themselves and their children without having to engage in smartphone use behaviors during non-use times.In addition, the study found that younger children were less likely to feel obligated to "perform" for the wearable camera, and that parents could capture their children's facial expressions and joyful moments in real time.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.066
GPT teacher head0.320
Teacher spread0.254 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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