Bibliographic record
Abstract
This project will address a problem drawn from the domain of software engineering: visualizing the history of source code. To gain understanding of the decisions and developmental steps that led to the current state of a piece of source code, a developer may need to consult several previous revisions of the code using a revision control system such as CVS. Current integrated development environments (IDEs) such as Eclipse make it easy to compare two revisions of a source code file. However, the developer may need to compare several past revisions to gain the desired understanding of the code’s evolution. Many-revision comparison is not, to my knowledge, well supported by current IDEs. Research in software visualization systems has explored some techniques for displaying software evolution, and I hope to build on these existing approaches. Any codebase with a significant revision history (and probably, though not necessarily, multiple contributors) is a potential dataset for this project. A particular dataset I may use is the open-source JQuery project from the UBC Software Practices Lab, with which I have some past experience as a developer. 2 Personal Expertise I have 20 months of employment experience (16 co-op, 4 as an undergraduate RA) in software development
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.020 |
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".