Enabling Scalable Proactive Workspaces With Environment-Wide Context
Bibliographic record
Abstract
Developers use a variety of independent tools to build modern software systems. However, coordinating these tools is a manual process that is laborious and extraneous to the developer's main task. Due to the diversity of projects and tasks that developers work on, one unified tool will never be able to solve this unnecessary work for all developers. Prior work has demonstrated that tools can be integrated through a shared context, which reduces coordination, navigation, and configuration burdens for developers and ultimately leads to higher productivity. These coordinated tools are better able to adapt their behaviour to the developer's current needs. Unfortunately, while these coordinated tools enable the developer to focus on the key aspects of their task, every tool must be directly integrated with every tool. This tight pairwise integration of tools is not scalable as new tools continue to be created to help developers manage ever-increasing software complexity. Reflecting on the scalability challenges of previous integration approaches, this paper introduces a vision for Proactive Workspaces that provide a mechanism for tools to publish and subscribe to context in a scalable way. Through a small feasibility study with 17 developers, we found that this lightweight mechanism enables tools to adapt to the developer's needs as they work, decreasing the overhead associated with coordinating multiple independent tools while increasing developer productivity by enabling them to focus on the important parts of their tasks.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".