Bibliographic record
Abstract
Racism is something that is learned. People aren’t born with racist ideas or attitudes. It doesn’t always come from a malicious place. Ignorance can play a big role in racial attitudes. But sometimes harm is intended. 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 police forces have been censured for racist policies. In 2016, one fifth of Canada’s population was born in another country. By 2036 it could be 25%. Now is the time to improve discourse around racism and change the narrative. This project will show how racism is institutionalized in our health care, judicial, and education systems. How even how much money you can earn can be impacted by your race, and why cultural appropriation is a big deal, and can affect everyone. The goal of this project is not to give concrete answers to some really complicated questions. What we hope to do is 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–because the only way to stop racism is to help people understand racism. Ideas of racism go as far back as humanity itself, but identifying why it happens, and what individuals can do to mitigate racism, might change the future. To start things off, we have a few clips of people sharing what they believe are common misconceptions of racism…including the fact it doesn’t exist. Below the clip you can access each section of this project by clicking on the red boxes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".