Does the home literacy environment matter: Reading achievement of immigrant and emergent bilingual students in Canada
Bibliographic record
Abstract
As a critical means of communication, literacy is a highly rewarded skill in the workplace. It is widely accepted that literacy development depends on both external and internal factors. One external factor, the Home Literacy Environment (HLE), has been shown to significantly influence literacy growth. Using a student sample (N = 7,552 unweighted observations) from the 2016 Canadian Progress in International Reading Study (PIRLS) exam, we conduct a multiple regression analysis to address the following questions: (1) Is there a relationship between the home literacy environment and reading achievement among all students? and (2) Is this relationship different for students with immigrant or emergent bilingual status compared to Canadian-born students who speak the language of the test? Results indicate a positive and significant association between several elements of the HLE and reading achievement – particularly with formal HLE activities, student readiness, and positive student reading perceptions. We found no association between PIRLS scores and informal HLE activities, attending pre-primary education programs, or school context variables. The significance of these findings extends across subgroup status. Our results suggest that policymakers should allocate public resources to programs that support, develop, and sustain the Home Literacy Environment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".