Resolving issues and concerns of ESL reading teachers through professional collaborative practices / Zuraimi Zakaria and Esther Care
Bibliographic record
Abstract
Reading is perhaps the most important academic skill that any learner should acquire. In fact, the ability to read and write in the early years of schooling is extremely crucial. Otherwise, children would have very slim chances of ever becoming fully literate (Willows, 2008). Yet a review on reading related literature suggests that the worldwide rate of literacy (or the lack of it) is alarming. Dagge (2007) states that Portuguese students read very little and often grapple with textual information. Moats (1999) however, points out that elementary students struggle with reading problems, which prevent them from independent reading or from having any enjoyment from reading. The rate of reading of Hispanic, African-American, limited English speakers and children living in poverty in the United States is high, ranging from 60 to 70%. The statistics of illiteracy is so alarming that the National Institute of Health, United States considers reading development and reading difficulty as a major public health concern (Moats, 1999). In Australia, 20% of its citizen aged between 15 to 74 are reported to have 'very poor' literacy skills, and an additional 28% are identified as having difficulties dealing with printed information (National Inquiry into die Teaching of Literacy, 2005). Willows (2008) adds that about 5%. of children in Canada are identified as having serious problems with reading, and up to 40% of primary school students would have fallen behind in reading by the time they end their primary years.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".