Bibliographic record
Abstract
ABSTRACT Knowing 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".