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Record W4313448026 · doi:10.32920/21758279.v1

The infant's face diet: Data on 3-month-old infant-perspective experience with faces video-recorded in their typical, daily environment

2022· preprint· en· W4313448026 on OpenAlexafffund
Nicole Sugden, Margaret C. Moulson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsToronto Metropolitan UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPerspective (graphical)PsychologyDevelopmental psychologyNeighbourhood (mathematics)Consistency (knowledge bases)Computer scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

<p> </p> <p>This dataset represents face experience coded frame-by-frame from nearly 170 hours of infant-perspective head-mounted-camera video, recorded during their daily life by 40 3-month-old infants. It includes information about the identity of the face (e.g., caregiver, relative), length of time the face was in the field of view, location in which the face occurred, and descriptions of the situation in which the infant experienced the face. Demographic information (e.g., age, gender) about the infants who recorded the videos is also provided. For elaboration on data collection methodology, interpretation, analysis, and discussion of early face experience captured by this dataset, please see our paper <em>These are the people in your neighbourhood: Consistency and persistence in infants' exposure to caregivers', relatives', and strangers’ faces across contexts</em> [1].</p>

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)0.003
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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.008

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 head0.069
GPT teacher head0.388
Teacher spread0.319 · 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
GenreDataset

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 routes2
Has abstractyes

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