Theorizing & Researching <i>Class</i> for Broadening Participation in Computing Efforts
Bibliographic record
Abstract
Race and gender are crucial concepts in the computer science (CS) education research community's broadening participation efforts and scholarship. They help to critique the ways that white supremacy, anti-Blackness, and patriarchy structure and discipline CS classrooms and workplaces. In addition, attention to race and gender has helped to reimagine and redesign CS education to be more culturally responsive and sustaining for marginalized students. This panel builds on these foundational efforts by starting a conversation about what a more intentional focus on the concept of class and its connections to political economy can offer researchers and educators who are committed to more race and gender inclusivity, diversity, and equity in CS education across primary, secondary, and post-secondary levels. What might the concept introduce into intersectional analyses of the exclusionary structures of the education-to-workforce CS ''pipeline''? How might class be defined to help redesign CS education to affirm the identities of racially marginalized students from working class communities? How might the language of class provide new insights into the ways that racism, sexism, and ableism shape CS education? What might the operationalization of the concept help to reveal about the economic interests that underpin mainstream CS curricula and education policies? And how might class help CS professionals and educators understand their own social positions? Through a discussion with researchers and practitioners from different disciplinary backgrounds and theoretical orientations, this panel seeks to provide a foundation for more intentional and rigorous engagements with the concept of class within the CS education research community.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".