DiNa Framework and Prototype to Support Collaboration in the Wild
Bibliographic record
Abstract
Much of the available collaboration support tools focus on sharing of documents and managing projects that require planned activities. These tools fall short in meeting principle of least effort or taking into account of the reality of complex work patterns. We propose DiNa framework and system architecture for a topic centric as opposed to document-centric collaboration system using readily available devices. DiNa aims to complement existing approaches. Our primary goal is to seek answers for how these devices can better support collaboration without overloading the workflow. After a literature review and roleplaying exercises, the prototypes we developed demonstrate new interaction techniques for defining topics and address them in collaborators’ own terms. It uses different visualizations of the artefacts and their association with the topics, among which is a scalable timeline interface accessible from different platforms, to make the artefacts collected more meaningful in a given context. In this paper we present our recent prototype as a proof-of-concept and its initial evaluations followed by the lessons learnt from our studies on supporting collaboration in the wild. The evaluation outcome is suggestions for improving DiNa-based systems for effective collaboration.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".