Bibliographic record
Abstract
A recurring motif in recent scholarship in the computing ethics and society studies (CESS) subfield within computing have been the calls for a wider recognition of the social and political nature of computing work. These calls have highlighted the limitations of an ethics-only approach to covering social and political topics such as bias, fairness, equality, and justice within computing curricula. However, given the technically focused background of most computing educators, it is not necessarily clear how political topics should best be addressed in computing courses. This paper proposes that one helpful way to do so is via the well-established pedagogy of citizenship education, and as such it endeavors to introduce the discourse of citizenship education to an audience of computing educators. In particular, the change within citizenship education away from its early focus on personal responsibility and duty to its current twin focus on engendering civic participation in one’s community along with catalyzing critical attitudes to the realities of today’s social, political, and technical worlds, is especially relevant to computing educators in light of computing’s new-found interest in the political education of its students. Related work in digital literacy education is also discussed.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| 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; 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".