Black students and higher education: dropping out of community college
Bibliographic record
Abstract
Given the extent of this phenomenon, the drop out problem represents a major failure of the higher educational system, affecting not just the individual or the Black community in particular but the society at large and indeed the country as a whole. Dropping out represents a major loss of financial and personal investment of time and resources for Black students. Also included are recommendations for drop out prevention. The study utilizes an anti-racist education framework that focuses on the lived experiences of minorities in terms of racism and social oppression. Seventeen Black youth volunteered to participate in the study. Using semi-structured interviews, information was elicited that provides insights into how schooling and education function to disengage some students. Findings reveal that many factors impact the decision to drop out. These factors include finances, socio-economic status, parental involvement, teaching, learning, academic preparedness, and administration of education. Race although not clearly identified by most participants as a factor is still considered a serious issue worth pursuing by the researcher. The students' narratives are presented, providing valuables insights into the thinking of those who drop out. A considerable body of research has been devoted to finding out why students drop out of college and how they can be prevented from doing so. This study explores the reasons why Black students in Toronto drop out of college. The major objectives of this study were to isolate and identify factors related to drop out behavior, to examine the perception that Black students have about dropping out and to develop some preliminary ideas regarding what can be done to minimize attrition behavior, especially among Black students.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".