Cultural alloys and heterogeneous mixes: Contextualized and comparative language differences in literacy assessment of U.S. and Canadian youth
Bibliographic record
Abstract
The United States and Canada, two countries known to have large immigrant populations, have long since reflected a dichotomy, where Canada is generally perceived to be a country with language policies that demonstrate its receptiveness to embrace multiculturalism in schools and classrooms. In contrast, the United States has consistently espoused the notion that one is “American first” and one’s cultural identity follows behind. It is within this context that the following study examines the difference in reading literacy performance between youth in the U.S. who self-identify as native English speakers and those who self-identify as non-native English speakers on the PISA assessment. The study also explores the difference in reading literacy performance among Canadian youth who self-identify as native English speakers, those who self-identify as native French speakers, and those who self-identify as neither native English nor native French speakers on PISA. Implications for policy, practice and society are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".