Gender and Language Issues in Assessing Early Literacy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".