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Record W2057341999 · doi:10.1177/0734282908314105

Gender and Language Issues in Assessing Early Literacy

2007· article· en· W2057341999 on OpenAlex
Sarah N. Harper, Janette Pelletier

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2007
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTera-LiteracyPsychologyReading (process)Meaning (existential)AlphabetDevelopmental psychologyLinguisticsComputer sciencePedagogy

Abstract

fetched live from OpenAlex

The study investigated gender and language group differences in children's performance on two versions of the Test of Early Reading Ability (TERA-2 and TERA-3). Two groups of children consisting of girls and boys and English first language (L1) and English language learners (ELL) participated in the study. Children in Group 1 completed the TERA-2, in which standard procedures involve obtaining a total score of children's early reading ability. Alternatively, children in Group 2 were administered the TERA-3, which yields measures of children's ability on three individual subtests (alphabet, conventions, and meaning). Results showed that gender and language group differences on the TERA-2 were not evident. However, L1 children outperformed ELL children on the meaning subtest of the TERA-3, while showing no differences on either alphabet or conventions. The findings speak to the importance of measuring individual components of early reading to assess children's emergent literacy.

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)0.002
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 score0.177
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.028
GPT teacher head0.465
Teacher spread0.437 · 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