Bibliographic record
Abstract
In today's educational systems, all students face obstacles and challenges.African Canadian students face unique challenges.This study investigates the perspectives of successful African Canadian students about their educational programs in Nova Scotia, Canada.The purpose of this study is twofold.The first objective is to examine the background and demographic characteristics, and socio-cultural experiences of ten African Canadian students in Nova Scotia.The second objective is to discuss the obstacles, challenges and strategies for success.The voices of these students provide insights into successful strategies for all students.The methodology included contacting ten African Canadian students at diverse levels in public education in Nova Scotia, to interview the participants by asking questions about their background, the obstacles and challenges which they have faced in education, and the strategies which have enabled them to be successful.Interviews are completed, and strategies for success have been analyzed.These data indicate that students have faced many challenges.Furthermore, these data indicate that facing the challenges have empowered the participants to devise strategies which have enabled them to be successful.Critical factors for their success include: family support; role modeling; caring educators; infusing African heritage into the curriculum; having someone believe in them, knowledge and pride in Black cultural and racial identity; and self-motivation.The author suggests that an improved understanding of the lived educational experiences of African Canadian students hold implications for all students and educators.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".