Adding an All Year Long Black History Class to High School Curriculum
Bibliographic record
Abstract
Using qualitative research design, with three Black youth participants who are in grade 10-12 and have attended high school in Ontario, this study answered the main research question “What are the perspectives of Black students about adding a year-long Black History class into high school curriculum?” This research seeks to understand the extent of Black history content incorporated into the school curriculum i.e., if Black history is taught, at all, frequency and duration in high schools. The study used ethnography research design in order to understand and shed light on the cultural experiences and meaning making responses of three Black youth between the ages of 16-21 years old about their high school curriculum. Ethnography is salient for this study because it works best for people who share similar experience and culture. Due to COVID-19, semi-structured interviews, using five open-ended questions were conducted on the Zoom platform, to answer the main research question. This paper discusses how Black students feel about Black History Month, and their opinions about adding a year-round Black history class to learn (and teach) about the histories of Black people. Afrocentric theory and perspectives were used to frame this research as they help facilitate conversations about historical trauma and racial disparities (Whitehead, 2018). This study has the potential to provide race-based data to advance advocacy for enhancing the current high school curriculum, informing meaningful pedagogical practices and contributing to the arguments for hiring more Black teachers in high schools to meet the needs of Black students and align with the demographic composition of schools in Ontario.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".