Racialized youth in the public library: Systemic racism through a critical theory lens
Bibliographic record
Abstract
Public libraries are on the frontline of serving underprivileged groups like racialized youth and help them to mitigate social inequities that manifest in negative outcomes like education gaps, underemployment and access to safe and affordable housing. Although racialized youth account for half of the youth population in Canadian cities like Toronto, their experience in public libraries is an unstudied area of Canadian LIS scholarly and professional research. Existing research approaches youth as a homogenous group in terms of age and biological stages and does not account for race, class, and urbanism. However, racialized youth face different challenges in which race and systemic racism are a facet of everyday life. This work aims to reverse racial neutrality in public libraries by demonstrating how ambivalence about race perpetuates systemic inequalities and the disengagement of racialized youth. It draws on interdisciplinary research to show how the race-blind approach is not reflective of the needs of communities being served. Using a Critical Race Theory (CRT) framework, it shows that public libraries can implement processes to gather race-specific data under the recently-implemented Anti-Racism Act (2017). This will provide a contextual understanding of the racial make-up of users and provide a valuable frame of reference to support efforts to build stronger and more effective relationships.
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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.031 | 0.080 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".