OER for Ethics and Computing Open Access Collection
Bibliographic record
Abstract
Coverage of ethics and computing is proliferating at universities, at both undergraduate and graduate levels. This includes standalone courses, and incorporation of ethics into technical computer science and related courses. Most of these courses, particularly the standalone ones, make extensive use of recent media articles, papers, videos, and other resources about issues related to ethics and computing. Thousands of such media articles alone are published annually. There is enormous duplication of effort by people who are teaching these courses, as discovering these resources is not always an easy process. The Association for Computing Machinery (ACM) Task Force on Ethics and Computing Education has developed an initial categorized open access collection of the titles and links to articles and other resources related to ethics and computing. Each reference in the collection is categorized by the most relevant technical topic. The collection will be updated regularly using a mechanism whereby people can submit suggestions that will be vetted by individuals knowledgeable in the field. It will be publicized so that educators teaching ethics in computing courses and units will be aware of this collection and how to access it. Educators who find novel ways to use the repository also will be encouraged to submit their experiences to EngageCSEdu. This work is informed by [1], which describes a crowdsourced spreadsheet of tech ethics courses and syllabi, as well as a subsequent analysis of what is taught in these courses [2]. Our aim is to provide a sustainable, evolving set of resources for such courses within the context of EngageCSEdu. In addition, just before the final submission of this paper we became aware of Computing Ethics Narratives [3], an excellent and related collection of resources produced as part of a project funded by the Mozilla Responsible Computer Science Challenge. We are cooperating with the creators of this resource moving forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.410 | 0.377 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".