Many Worlds of Ethics: Ethical Pluralism in CSCW
Bibliographic record
Abstract
Although CSCW has shown a strong interest in diversity and inclusion, the literature predominantly reflects ethics rooted in Western universalism, modernism, scientism, and Euro-centrism. Consequently, CSCW theories and practices tend to marginalize millions of people worldwide whose ethical perspectives do not align with the narrow focus of ethics and values within CSCW. In an effort to embrace ethical pluralism within CSCW, we propose a day-long hybrid workshop in CSCW and invite researchers and practitioners to initiate conversations centered around three themes: (a) foregrounding ethical diversities, (b) adapting diverse ethics, and (c) addressing challenges, barriers, and limitations associated with incorporating plural ethics into CSCW. Through this workshop, we aim to bring together CSCW scholars and practitioners, fostering a community that advocates for and advances the cause of pluralism in socio-technical systems.
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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 | 0.146 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.026 | 0.123 |
| Scholarly communication | 0.046 | 0.045 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".