Bibliographic record
Abstract
The Blurred Lines of Racism is a multimedia website focusing on an issue that is both polarizing and highly misunderstood. Although Canada is seen as being very tolerant, hate crimes here rose sharply in 2017–up 47% over the previous year. Our First Nations’ communities face serious issues with water quality and access to care, and multiple police forces have been censured for racist policies.\nThis project examines the psychology of racism and how racism is institutionalized in our health care, judicial, and education systems. It identifies where racism comes from, where it still lives, and sheds light on debates surrounding flashpoints such as the use of the “N” word, hijabs, Islamaphobia, Holocaust denial, white privilege, hi-tech racism, and cultural appropriation. As Canada is closely tied to the U.S. by location, media systems, and some aspects of culture, we also touch on issues south of our border.\nData housed within the site come from multiple sources and modes of delivery, including on-camera interviews with subject experts, people who have faced racial discrimination/bias, and even personal stories from students who developed the site; for example, in a short podcast, our project coordinator shares how she was told by relatives she wasn’t as “pretty” as her sisters because her skin was too dark. We also use graphics to better explain statistics, unpack and link to a variety of academic and journalistic articles and videos related to racism, and use social media comments to capture current discourse.\nThe goal of the project was not to give concrete answers to some really complicated questions, but to help people understand what it feels like when you are the person who’s the target of racist behaviour and how racist behaviour emerges. We see it as an important resource to both shed light on the full impact of racism and to provide access to ideas on how to effect positive change.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.516 | 0.234 |
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".