Literacy Gatekeepers in the Ontario Education System. Why ESL Students Fail? A Bordieuan Perspective
Bibliographic record
Abstract
Ontario's education system aims to improve children's literacy levels who hail from diverse backgrounds. Schools must tailor their program layout to their students’ unique needs. Immersion is one of them. As with submersion, instruction occurs in the second language (L2), but there are significant differences. ESL students experience linguistic barriers. According to Migration Matters (June 2017), by 2022, 78% of job openings will require some post-secondary training or university degree. Many immigrants do not have the skills necessary to succeed, and these percentages have not improved. There is a widening incongruence between the complexity of the needs of ESL learners and the availability of ESL [English as a Second Language] services in Canadian schools. School boards across Canada have steadily reduced ESL services over the years (Nichols et al., 2020). Schools are not meeting the language needs of immigrant youth in Ontario, where 29.1% of the population, the highest of any province, is foreign-born (Government of Ontario, 2017). In 2017, 63% of Ontario's elementary schools and 58% of secondary schools had English language learners (ELLs). However, only 38% of English-language elementary schools had ESL teachers (People for Education, 2017). 20% of elementary schools and 31% of secondary schools have no formal process for identifying ELL students (People for Education, 2015), which is the first step to placing students in ELL services. Students’ diverse needs must be met for them to acquire necessary literacy skills. Literacy is a civil right—no one should leave the school system as an illiterate person.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".