Intersections of race, COVID-19 pandemic, and food security in Black identifying Canadian households: A scoping review
Bibliographic record
Abstract
Although studies have identified food insecurity as a racialized inequity issue disproportionately affecting Black identifying Canadians, research exploring how anti-Black racism across multiple systems create inequities including increased risk for food insecurity among African Caribbean Black identifying households in Canada, is limited. Using an intersectionality lens, this scoping review addresses this knowledge gap by elucidating the intersectionality of race with multiple social determinants of health that directly and indirectly impedes Black people (both of African and Caribbean descent) from accessing adequate and appropriate food, resulting in disproportionate health and social outcomes. Critical analyses of twelve journal articles identified systematically and the review of government and organizational reports and websites reveal that food security in Black identifying individuals in Canada is a racialized emergent public health issue rooted in structural and systemic racism that intersects with multiple determinants of health to produce grave social and economic inequities. The recent COVID-19 pandemic intensified these inequities by increasing food insecurity in Black identifying households in Canada. Cultural food security, referring to the ability to acquire and access culturally appropriate foods to one’s ethnic origins as fulfilment to cultural identity, is an interrelated and foundational pillar to food security yet one that is grossly unacknowledged in current actions. National policies are thus needed that recognize cultural food security, and address root causes through increased social support and sustainable food systems. A reasonable first step to ensure the cultural relevance of policies and initiatives is the active engagement of Black communities.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".