Bibliographic record
Abstract
Computer supported cooperative work (CSCW) allows large groups of people to collaborate more efficiently through the use of information systems. Current research in the area has shown that role based collaboration (RBC) is a powerful tool to implement a complex CSCW system. It allows the separation of concerns and the flexible definition of rights and responsibilities. However, RBC has not gained wide spread acceptance, partly because there is no robust proof of concept. In this paper, we implement a framework based on the E-CARGO model using the Java language that can support many types of user environments (e.g. Web-based, Eclipse Rich Client Platform, Console) and provide a tool for adding new collaborative activities to the system. We then give examples of how typical collaborative activities are added to the system and conclude by investigating the pros and cons of such a method and suggesting further steps to create robust RBC systems. This new framework hopes to allow RBC systems to be more widely accepted in practice. The main contributions of this paper include the first consistent tool for collaborative systems that uses RBC as a base and a novel method for adding new types of collaborations to the role-based CSCW system
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".