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?
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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; both teacher heads agree on what is shown here.
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".