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Record W4415741924 · doi:10.1080/02702711.2025.2579301

Fast Mapping with Orthographic Support in School-Age Children: Exploring Statistical Patterns

2025· article· en· W4415741924 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReading Psychology · 2025
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
FundersYork University
KeywordsOrthographic projectionStatistical analysisStatistical learningCluster groupingTask analysisStatistical model

Abstract

fetched live from OpenAlex

This study explored the possible influence of phonotactic and orthotactic probability on fast mapping in school-age children with and without a developmental disorder. Participants were exposed to pictures of novel objects paired with spoken and written novel words, which varied in phonotactic and orthotactic probability. Fast mapping was examined with gaze behavior (proportion of looking time) and accuracy (picture identification, orthographic identification, picture naming). Participants were highly accurate during picture identification and orthographic identification, and less accurate during picture naming. Fast mapping was somewhat influenced by phonotactic and orthotactic probability. During picture identification, children were less likely to be accurate in trials with novel words with low orthotactic probability. Gaze behavior during exposure and response accuracy varied in terms of language ability and age.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.541

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 head0.025
GPT teacher head0.282
Teacher spread0.257 · 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