Bibliographic record
Abstract
What is antiracism and why should we consider it one of the virtues, like courage or honesty? Getting Ethics to Work’s resident ethicist Andy Cullison and producer Kate Berry explore the ways in which antiracism--as defined by scholars like Angela Davis and Ibrham Kendi--is a virtue. On this episode and every episode, we dig into the complicated moral issues people face in the workplace. If you have a workplace dilemma you need some help with, send your story to our producer Kate at katherineberry@depauw.edu. For this episode’s transcript, click here. Shownotes “Being Antiracist”: Materials from the National Museum of African American History and Culture How To Be An Antiracist by Ibram X. Kendi Antiracism defined by the Alberta Civil Liberties Research Centre Virtue Ethics Aristotle’s Virtues and Vices What is a White Savior? Materials for Talking about Race and Racism at Work "Confronting Racism at Work: A Reading List How to talk about racism at work “How Should You Be Talking With Employees About Racism?” HBR Ideacast “Talking about Race at Work" "It's Time to Stop Talking about Diversity at Work and Start Talking about Race" "How to Begin Talking about Race in the Workplace" Credits: Thanks to Smallbox for designing our logo and website. Thank you to Brian Price for editing and mixing each episode. “Brass Buttons” by Blue Dot Sessions From www.sessions.blue CC BY-NC 4.0 To contact us, email katherineberry@depauw.edu
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".