Bibliographic record
Abstract
ABSTRACT \n \nKnowing that the decision to leave school before graduating has a dramatic effect on the lives of the people who make this choice, why would anyone leave school before graduating from high school in Canada? School is important, and parents bring their children to school brimming with the hope their children will do well. Notwithstanding this hope, every elementary school classroom in Canada has two or more children who are likely to drop out of school. If these children live in an inner-city or a reserve, the likelihood they will not graduate increases dramatically. In fact, Grade 1 teachers who teach on reserves in Canada can look at the students in their classrooms and know that more than half of these students are unlikely to graduate. This project is an attempt to address this issue by understanding more about why students drop out, particularly students who are disadvantaged due to low socioeconomic status (SES) and/or Aboriginal status. It consists of a general introduction to the topic (Chapter 1), an extensive review of theories with respect to dropping out (Chapter 2), two workshops, one to increase understanding of these theories and another that addresses the effects poverty has on the neurocognitive development of children (Chapter 3), and reflections on the process (Chapter 4). It is largely intended for teachers and administrators who would like to understand the dropout phenomenon and increase graduation rates for disadvantaged students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".