Bibliographic record
Abstract
Over the past three decades it has become increasingly common to include social and professional issue topics within undergraduate computing programs. A decade ago, a consensus had emerged around best practices in teaching these topics: namely by covering a professional code of conduct along with a handful of ethical theories and then applying them to computing or workplace dilemmas and choices. Yet despite the successful wide-scale inclusion of ethics instruction within most computing programs, the perception persists that the societal harms of computing remain undiminished. This paper argues that ethics was never the answer to this problem. Addressing the social consequences of computing requires recognizing that computing is deeply enmeshed in political issues, and that the route to addressing this in our curricula is to integrate political topics within them. We can learn effective ways for doing so by making use of pedagogical approaches already pioneered within digital literacy and citizenship education which prioritize questions around justice, equity, and participation. These approaches also focus on engendering critical perspectives towards the students’ digital and non-digital ecosystems as well as encouraging democratic activism and civic engagement with their communities. These citizenship approaches can help our computing curricula better achieve the goals that initially motivated the inclusion of ethics: to help our students play a part in constructing a better world.
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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 | 0.048 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.093 |
| Scholarly communication | 0.015 | 0.044 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".