Bibliographic record
Abstract
Manitoba’s dropout rate was nearly 3% higher than the national average of 8.5%, at 11.4%; this was the second highest rate among the ten provinces. The Government of Manitoba published the six-year high school graduation rate at 83.2% for the end of the 2017 school year (Government of Manitoba, 2017). This suggests the actual dropout rate may be significantly higher. Literature indicates there are several types of interventions schools can use to help mitigate many of the risk factors for students dropping out. Three common interventions in schools are food programs, mentoring programs, and the development of strong, positive student-teacher relationships. The purpose of this qualitative study is to gain an understanding of the perspectives of high school resource teachers in Manitoba regarding these interventions through one-on-one interviews. The resource teachers who contributed to this study reaffirmed the results of previous literature and the importance of these interventions for the at-risk student population. The results indicate Manitoba high schools have not implemented these interventions formally and/or they are not consistent between high schools. This study demonstrates the need for the implementation of formal food and mentoring programs, as well as policies and training for teachers to encourage the development of positive student-teacher relationships, with the end goal of reducing Manitoba’s high school dropout rate.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".