The Role of After School-Programs in Promoting Youth Inclusion in Rural and Small Communities: The Case of the Fusion Youth and Technology Centre, Ingersoll, Ontario
Bibliographic record
Abstract
Rural communities face many challenges including the retention of their youth. Rural youth also face challenges, particularly in comparison to urban youth. Rural youth experience higher levels of morbidity and mortality as a result of risky behaviours, and face a different set of challenges in regard to issues of social inclusion/exclusion when compared to urban youth. One way in which these risks and challenges can be mitigated is through the provision of afterschool programs. This study begins by asking the question what impact does the provision of the Fusion Youth and Technology Centre, a progressive afterschool program, have on rural youth's feelings of social inclusion/exclusion? A qualitative study was undertaken and the results suggest that the provision of an afterschool program does provide an inclusive environment where youth feel cared for and connected, are allowed to explore and develop skills, competencies in a safe and secure environment that they experience as 'their place'. However, they still report feeling excluded from the larger community. Keywords: rural youth; afterschool programs, social inclusion, social exclusion
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".