THE GRID SHARED DESKTOP: A BOOTSTRAPPING ENVIRONMENT FOR COLLABORATION
Bibliographic record
Abstract
The paradigm shift from an information-sharing infrastructure (i.e., the Web) to a resource-sharing infrastructure (i.e., the Grid) has open new perspectives for CSCL (Computer Supported Collaborative Learning). With Grid, it is now possible to envisage a scalable infrastructure that offers live collaborative environments in a secure manner. The Grid Shared Desktop (GSD) is one such collaborative environment that inherits from the desktop as a natural human- machine interface to become a multidimensional humans-to-humans interface via several dedicated desktops. The success of such environments depends upon several considerations that we will develop here. We have not so far identified any equivalent solution that can fully suit CSCL requirements. In fact, all solutions are either ad-hoc system-oriented or they are not scalable as they cannot manage resources efficiently. In order to satisfy the CSCL needs, we propose a platform-independent solution that benefits from the intrinsic advantages of the Grid technologies. This goal is greatly enhanced thanks to the ability of Grid to support stateful, dynamic services. In this paper, we also tackle the problem of bootstrapping and supporting a collaborative environment. As we target communities of non-computer-literate people, we investigate easy-to-use and flexible solutions. Finally, we present our latest experimental case study with the GSD in the context of collaborative construction of a shared ontology.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".