Bibliographic record
Abstract
Purpose – This paper aims to demonstrate that computing social issues courses are often being taught by articulating the social impacts of different computer technologies and then applying moral theories to those impacts. It then argues that that approach has a number of serious drawbacks. Design/methodology/approach – A bibliometric analysis of ETHICOMP papers is carried out. Papers from early in the history of ETHICOMP are compared to recent years, so as to determine if papers are more or less focused on social scientific examinations of issues or on ethical evaluations of impacts of technology. The literature is examined to argue the drawbacks of the impact approach. Findings – Over time, ETHICOMP papers have moved away from social scientific examinations of computing to more philosophic and ethical evaluations of perceived impacts of computing. The impact approach has a number of drawbacks. First, it is based on a technological deterministic style of social explanation that has been in disrepute in the academic social sciences for decades. Second, it uses an algorithmic approach to ethics that simplifies the social complexity and uncertainty that is the reality of socio-technological change. Research limitations/implications – The methodology used in this paper is limited in several ways. The bibliometric analysis only examined five years of ETHICOMP papers, while the literature review focused on published computing education research. It is possible that neither of these forms of evidence reflects actual common teaching practice. Practical implications – It is hoped that the arguments in this paper will convince teaching practitioners to modify the way they are teaching computing social issues courses: that is, the authors hope to convince educators to add more focus on the social context of computing. Originality/value – The use of bibliometric analysis in this area is unique. The paper’s argument is perhaps unusual as well.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".