Virtual Community Building and the Information Society
Bibliographic record
Abstract
The research and development on spoken dialog systems embraces technical, user-centered and business-related perspectives. It brings together stakeholders belonging to distinct job families, therefore prone to different traditions and practices. When assessing their contributions, as well as the final solution, they conduct very nomadic evaluation protocols. As a result, the field is eager to set up norms for evaluation. Contributions abound in this way. However, despite standardization exercises, we believe that the absence of common conceptual foundations and dedicated knowledge creation spaces frustrate the effort of convergence. The chapter therefore presents an application framework meant to rationalize the design of evaluation protocols inside and across project teams. This Multi Point of VieW Evaluation Refine Studio (MPOWERS) enforces common models for the design of evaluation protocols. It aims at facilitating, on the one hand, the individual evaluator-users task and, on the second hand, the emergence of (first virtual, then maybe real) communities of practice and multidisciplinary communities of interest. It illustrates how implementing shared knowledge frameworks and vocabulary for non-ambiguous asynchronous discussions can support the emergence of such virtual communities.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".