Examining Environmental Risk Factors and Opportunities for Intervention in the Emergent Literacy Development of Low-SES Students in Windsor, Ontario
Bibliographic record
Abstract
Applying Bronfenbrenner’s Ecological Systems theory (1979), this study sought to understand environmental risk-factors and the interactions between them that construct emergent literacy development, as well as attitudes towards the literacy learning of low-SES children from disadvantaged neighbourhoods in Windsor, Ontario. Data collection for this qualitative study was conducted in the form of semi-structured interviews, organized and coded according to common themes and concepts for further analysis and interpretation. The perspectives of Early Childhood Educators (ECE) were sought to understand the challenges faced by children from low-SES families in gaining early literacy skills, due to environmental factors, and what implications this has for future success. Participants suggested that factors associated with home, school, childcare, and neighbourhood environments have the capacity to support or hinder the literacy development of low-SES children; furthermore, that when these environments operate in cohesion, they may have the potential to compensate for lacks in individual environments. The results of this study highlight reoccurring themes, such as the need for ongoing and sustainable partnerships between schools, childcare centres, and social services, as well as asset- based approaches to literacy learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".