Preparing community members to enact specific roles in emergency situations : a research agenda
Bibliographic record
Abstract
The model presented in figure 1 - the collaboration ontology roles game model (CORG) , is an adaptation of the SECI model proposed by Nonaka and Takeuchi in 1995. According to the authors, the process of knowledge creation is an iterative process which continuously cuts four modes of knowledge conversion: socialization (a meeting to share experiences), externalization (a map to formalize a new process), combination (of explicit knowledge in the real setting) and integration (or learning by doing) that is the ultimate goal for each individual willing to be prepared to face an emergency situation. The process of knowledge creation is described in detail by Nonaka and Takeuchi (1995) by considering the following steps: 1. Sharing tacit knowledge, 2. Creating concepts, 3. Justifying concepts, 4. Building an archetype and 5. Cross-levelling knowledge in the interorganizational network and its environment. Emergency situations – we are interested in this poster in a possible avian flu alert in Quebec – require collaboration between members of different organizations, agencies and communities (Daniels, 2007; Carver and Turoff, 2007). Recently, a new body of literature has emphasized the learning capacity of the adhocracy compared to automated responses from the hierarchy (Medonca et al. 2007). In this poster, we present our vision to develop a role-based simulation environment for the health care community and their partners to manage, on a collaborative basis, an extreme event such as avian flu.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".