Bibliographic record
Abstract
The productivity of software developers is under constant attack due to a continual inundation of information: source code is easier and easier to traverse and to find, email inboxes are stuffed to capacity, RSS feeds and tweets provide a continual stream of technology updates, and so on. To enable software developers to work more effectively, tools are often introduced that provide even more information. The effect of more and more tools producing more and more information is placing developers into overload. To combat this overload, we have been building approaches rooted in structure and inspired by human memory models. As an example, the Mylyn project packages and makes available the structure that emerges from how a programmer works in an episodic-memory inspired interface. Programmers working with Mylyn see only the information they need for a task and can recall past task information with a simple click. We have shown in a field study that Mylyn makes programmers more productive; the half a million programmers now using Mylyn seem to agree. In this talk, I will describe the overload faced by programmers today and discuss several approaches we have developed to attack the problem, some of which may also pertain beyond the domain of 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 | 0.027 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".