Computing trust: on writing ‘good’ code in computer science education
Bibliographic record
Abstract
What does it mean to produce trustworthy code for computer scientists? Based primarily on ethnographic fieldwork in an undergraduate computer science program in Singapore, this article explores what it means for computer science students to write ‘good code.’ In doing so, it explores the values that underlie ideas of trust in the computer science discipline. Drawing on the work of Rebecca Bryant, this article shows how, as students learn to become ‘good at’ writing code that is technically functional, aesthetically un-individuated, and decontextually efficient, they also learn to become ‘good’ computer scientists. These standards of good code are distributed across human and nonhuman actors and provide a framework for ‘trustless trust’ in code. That is, while computer science often assumes an omnipresence of mistrust, this article argues that the production of ‘good’ code and ‘good’ computer scientists works to build a system of distrust for computer scientists. At the same time, becoming a good computer scientist is intimately intertwined with students’ selfhoods, undermining the foundation of trustless trust even as the ideal of objectively ‘good’ and trustworthy code cuts this contradiction from view.
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 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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".