Family Literacy Programs as Intersubjective Spaces: Insights from Three Decades of Working in Culturally, Linguistically and Socially Diverse Communities
Bibliographic record
Abstract
In this article we draw on our three decades of work in culturally, linguistically, and socially diverse communities – an economically depressed, rural community in Eastern Canada, a culturally and linguistically diverse metropolitan area of western Canada and a First Nations community in north-western Canada – to document the development and evolution of a social-contextually responsive family literacy program. We propose that family literacy programs can build on the strengths that families bring and provide an intersubjective space where families and schools can share knowledge in a reciprocal, respectful manner. Over the last three decades or so, researchers have documented that families can play important roles in children’s early literacy development (Mui & Anderson, 2008; Purcell-Gates, 1996; Taylor & Dorsey-Gaines, 1988). Indeed, Goodman (1980) called the informal, often serendipitous literacy activities and events that occur in homes and communities the roots of literacy. Attempting to capitalize on this knowledge, educators have developed family literacy programs that aim to support families in increasing opportunities for young children to engage in literacy activities and events at home, to enhance their early language and literacy development. Converging evidence indicates that: these programs can have a positive effect on children’s language and 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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.041 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".