Sociocultural Variation in Literacy Development in Canada and the United States
Bibliographic record
Abstract
This chapter aims to synthesize evidence from large-scale studies on the magnitude of disparities in students’ literacy development between students from low socioeconomic and language minority backgrounds and their more advantaged counterparts in the United States and Canada, particularly on reading skills. Among various structural inequalities that are relevant to reading, the focus is on socioeconomic status (SES) differences and linguistic diversity (and their inter-relationships). It is explicitly acknowledged that extensive structural inequalities also exist for indigenous peoples in both countries across a range of education, health, and social outcomes. The focus is on language minority learners in this chapter primarily because the large-scale research that has examined disparities in reading development has provided valuable insight regarding students from immigrant backgrounds. By focusing the lens on reading development in language minority learners, we do not intend to minimize or obscure the very real barriers to equitable education in indigenous communities, nor do we intend to convey that structural forces that affect immigrant students can generalize to indigenous communities. Instead, we echo calls to address the sociocultural context of reading development and education more broadly in indigenous communities in both countries.
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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 | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".