A Conversation About Data on Race & Ethnicity Around the World
Bibliographic record
Abstract
The Black Lives Matter and Indigenous rights movements, as well as international migration in recent years have raised awareness of issues around inequalities because of race and ethnicity. In turn, this has prompted many organisations and groups such as IASSIST to reexamine their own understanding and knowledge, processes and practices. In response, the IASSIST Anti-Racism Interest Group was formed and brought together data stewards and librarians who had some or no prior expertise but who were interested in having a conversation about race and ethnicity in terms of data available for research and exploring how they could support the vital work in this field. This webinar took place on November 30, 2022, 11 am - 12:30 pm EST. It was organized by the Anti-Racism Resources Interest Group and the IASSIST Professional Development Committee and marks the beginning of that conversation with a focus on the data that is available for research. Bringing together a panel of data stewards and librarians from 4 countries - Canada, the US, the UK and Germany - this webinar aims to discuss and review these key questions: How are race and ethnicity recorded in the national Censuses and other key data sources? Have these definitions changed over time? What groups are identified, how much detail is available? What are some of the key issues with these data?
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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 | 0.044 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".