The Effectiveness of Embedded Values Analysis Modules in Computer Science Education: Replication & Expansion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the impact of embedding short ethics modules covering core concepts and methods of Value Sensitive Design (VSD) into computer science courses at the undergraduate level. The study will be conducted at Northeastern University (NU), where Ethics Institute instructional staff have been teaching ethics modules based on VSD as part of various courses run through NU’s Khoury College of Computer Science since the fall of 2019. The main research question is whether these embedded VSD modules are having a positive effect on certain ethically relevant attitudes. The present study attempts to replicate previous positive results reported in Kopec et al. (2022), as well as to compare the results from NU’s program and Toronto’s program reported in Horton et al. (2022), thus combining both sets of metrics into a new survey instrument.
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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 | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.025 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.030 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.011 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it