Sociological Imaginations for Anti-Racist Futures: An Interview with Dr Prudence Carter
Bibliographic record
Abstract
In this interview, Dr Prudence Carter, 2021–2022 President-Elect of the American Sociological Association, discusses how sociology can contribute to anti-racist futures across national contexts. Her insights point to the need for greater self-awareness in sociology regarding race and racism, for clarification of our aims and for better articulation and translation of popularized theoretical concepts, such as structural racism, to the general public. To achieve radical inclusion in the future, she highlights the importance of engaging in public and policy sociology, by explaining and substantiating policies and practices derived from our research. She also underscores the significance and value of comparative cross-national and multidisciplinary collaborative research. Most importantly, she brings to the fore the necessity of imagining new epistemological and methodological approaches to study the conditions that will enable our societies to attain equitable and anti-racist futures. Fundamentally, this involves extending our sociological imaginations.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".