College students from the margins: Journeys of persistence and early leaving
Bibliographic record
Abstract
The fundamental question guiding this research is how do marginalized college\nstudents describe their social and educational experiences before, during, and after a oneyear\ndevelopmental college program? The other questions of importance are how do\nmarginalized college students persist (or not) with their educational goals? What do these\nmarginalized students recommend to college educators and administrators to better\nsupport their social and educational goals?\nCritical ethnography is used within this study to better understand the school and\nnon-school related factors that contributed to success or withdrawal from a college\ndevelopmental program. Data was gathered by conducting narrative interviews with\nparticipants, by providing my personal standpoint and observations as a teacher in the\nprogram, and through critical engagement with policies and program documentation.\nThe 11 participants interviewed were formally enrolled in the College Bridging Program\n(CBP) within a college operating in an urban area in Atlantic Canada. Through critical\ntheory, this study relates the issues around class, socio-demographic background,\neconomic factors, academic preparedness/experiences, institutional and classroom\ncharacteristics, and degree of social engagement to the experiences of students as they\ntransitioned through the education system. Therefore, this study examines these issues from the perspective of a student in an effort to determine why marginalized college\nstudents leave or stay in post-secondary education, and what contributes to the success of\nmarginalized students.\nAdditional research on marginalized youth in tertiary education is necessary\nconsidering our current system was designed for a more traditional type of learner. There\nis pressure on tertiary institutions to provide supports and programs for marginalized\nstudents. There is a paucity ofresearch related to the voices and experiences of postsecondary\nmarginalized students from a wider variety of social, cultural, and educational\nbackgrounds. New knowledge from this research will add to our understanding of\nmarginalized students' perceptions of their educational and social pathways. By focusing\non the voices of students, educational institutions can place more emphasis on\nempowering, engaging, and including all students, which will strengthen the ability of\nmarginalized learners to achieve success, growth, and transformation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".