Exploring Schooling and Educational Attainment through the Experiences of Homeless Youth
Bibliographic record
Abstract
This research demonstrates that homelessness significantly impacts a young persons academic engagement and attainment. Education is a topic that remains at the core of many debates regarding young people, and is central to our understanding of what can lead to better employment and economic opportunities and overall well-being. However, youth who experience homelessness face extreme forms of social exclusion. Many come to the streets having to forego formal education. A high percentage of youth who become homeless are forced to leave school, and the complexities of homelessness disrupt their educational experiences. \n \nIt is unclear how the education system in Canada – a key statutory institution in young peoples lives – and the youth homelessness sector respond to the disengagement of these youth from school. In our common approaches to supporting homeless youth, Canadian aid agencies respond by providing emergency services, such as shelter, food, counselling, and other supports. Unfortunately, education is often not prioritized; rather, reactive emergency services adopt a neoliberal orientation towards young people to help them become independent, focusing on training them for the job market. The focus shifts to paid work and therefore self-sufficiency; as a result, education is considered a part of the young persons past rather than their future. \n \nThis dissertation includes analysis and research findings that are directly informed by surveys and interviews with 40 homeless youth from York Region, in Ontario, Canada. Their narratives reveal that it is nearly impossible to remain in school while homeless due to housing and familial instability, mental health challenges, and challenges in school. Social exclusion theory and a critique of neoliberalism highlights why young people get stuck in a cycle of poverty and homelessness, and a psychoanalytic lens considers how listening to stories of suffering may awaken the self-reflexivity needed to evoke a more active response. Alongside the voices of youth and other scholars doing this important work, I insist that the education system, the homelessness sector, and community-based services prioritize educational attainment for homeless youth; this may allow them to remain in school, move forward with their lives, and prevent them from winding up on the streets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".