Expanding opportunities for disadvantaged students to complete secondary and go onto postsecondary education: The effectiveness of Pathways to Education Program and its implications
Bibliographic record
Abstract
Children growing up in poverty have become a social issue worldwide and need to be helped on many levels. Research results have shown that children from disadvantaged background are at risk of ending up poor compared with children from affl uent background. A society would suffer a huge loss without any improvement in child poverty. Pathways to Education Program was developed to support disadvantaged students in Regent Park in Ontario, Canada in 2001. The program was created by the effort of Regent Park Community Health Centre to combat the low educational attainment of the youth in the community at the time. It is intended to help students from low-income families complete secondary education and go on to postsecondary education through four types of support: academic, social, financial and advocacy supports. The program largely depends on the commitments of many volunteers in the community as well as dedicated staff, who work closely with local schools and school district boards. The effectiveness of the program was revealed in a short period of time, and the program generated a wide public attention. The dropout rates in the community declined greatly and the proportion of students who moved on to post-secondary institutions increased significantly. The program has started expanding to other low-income communities across Canada. As of 2016, this integrated community-based program is delivered in 18 sites. Lately, due to the increase in reports and research on impoverished children, the Japanese society has begun to recognize reality of children living in poverty. The findings show that not only supporting disadvantaged students to enter a high school, but also preventing them from dropping out is important to eliminate individual and societal costs in the future. It has also been found that student’s positive relationships with dedicated adults are effective for students to build self-esteem and confi dence, and then contribute to make their positive attitude towards school. Pathways Program and its effectiveness suggest us that community based efforts like Pathways Program with multi-faced supports help disadvantaged students expand their opportunities to pursue a brighter future.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".