Bibliographic record
Abstract
The issue of truancy and early school leaving has been a longstanding social problem. The current high school drop out rate of thirty percent has government officials and educators very concerned about the future of our youth. A prevalent theme in the truancy literature is the impact that poverty has on children and families. but, there are many additional factors that contribute to truancy and understanding them assists in developing strategies and programs to address this problem. Research indicates that one caring adult and/or supportive environment can be a key factor for children overcoming tremendous adversity. Schools can be a place where children find caring adults and supportive environments. This qualitative study examined staff perceptions of the impact of an Attendance Incentive Program developed by an Attendance Counsellor in Southern Ontario. The findings of the study showed that Attendance Incentive Programs can, not only be an effective way to address truancy, but also provide positive outcomes. These included greater academic success, student pride, team building among staff and students and positive connections established between the school community and the business community who support the program. A significant matter in question is the sustainability of this particular program as it depends largely upon the efforts of the Attendance Counsellor. A recommendation for continued growth and development of the program includes the soliciting of parent and community volunteers to become involved and take on some of the numerous responsibilities. Although, the findings cannot be generalized to larger school populations they do reveal positive outcomes. Therefore, attendance incentive programs should be considered as an intervention strategy to address truancy.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".