Bibliographic record
Abstract
Research with open source software (OSS) raises the same ethical issues as other disciplines in which publicly released materials are the objects of study, and the creators of those materials are still living.These disciplines are literary and artistic criticism and public policy research.As El-Emam (this issue) mentioned there are also similarities to research employing internet newsgroup posts as data.The fact that the software engineers or programmers are still living is important since it raises the possibility that they may be harmed by the research.(Were they dead instead, research with OSS would more closely resemble archaeology, which raises dierent ethical issues.)As El-Emam noted analyses could rank the programmers according to the defect rate of their code, thus adversely aecting the careers of the worse programmers.Some readers may be of the opinion that this is perfectly acceptable from an ethical perspective, arguing that the better programmers should be rewarded and the worse programmers should be punished.However, this position assumes that the metric accurately captures the programmer's value, which may not be the case.For instance, one programmer's code may contain more defects than another's but may also be easier to ®x, maintain, modify, and re-useÐcharacteristics that were not captured by the metric but are nonetheless valuable.Additionally, the diculty of the coding tasks undertaken by each programmer may have varied greatly, such that defect rates alone do not adequately measure programming skill.Consequently, a metric-based ranking of programmers can be misleading, resulting in harm that is not a function of the programmer's true worth.The potential for harm is important because it increases the importance of obtaining informed consent.If the potential for harm were eliminated, the need for consent would be greatly reduced.To illustrate, consider a completely dierent research situation that would not normally require the informed consent of the subjects (45 CFR 46; Tri-council, 1998).Researchers place two telephone booths side by side.One is covered in grati and one is clean.The goal of the research is to determine whether the two telephone booths will attract an equal proportion of callers.Consequently, a researcher sits in view of the two phone booths and counts the number of people who enter each one.The researcher does not note any information that could be used to identify the research subjects.In such a case, the subjects cannot be identi®ed, reported data cannot be traced back to them, the Empirical Software Engineering, 6, 293±297, 2001.
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 | 0.202 | 0.418 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.046 | 0.075 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".