SUPPORTING COLLABORATIVE INTERPRETATION IN DISTRIBUTED GROUPWARE
Bibliographic record
Abstract
Collaborative interpretation occurs when a group interprets and transforms a diverse set of information fragments into a coherent set of meaningful descriptions. This activity is characterized by emergence, where the participants' shared understanding develops gradually as they interact with each other and the source material. Our goal is to support collaborative interpretation by small, distributed groups. To achieve this, we first observed how face-to-face groups perform collaborative interpretation in a particular work context. We then synthesized design principles from two relevant areas: the key behaviors of people engaged in activities where emergence occurs, and how distributed groups work together over visual surfaces. We build and evaluated a system that supports a specific collaborative interpretation task. This system provides a large workspace and several objects that encourage interpretation through emergence. People manipulate cards that contain the raw information fragments. They reduce complexity by placing duplicate cards into piles. They suggest groupings as they manipulate the spatial layout of cards and piles. They enrich spatial layouts through notes, text and freehand annotations. They record their understanding of their final groupings as reports containing coherent descriptions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".