Video conference platforms: A tool to foster collaboration during interorganizational in vivo simulations
Bibliographic record
Abstract
Inter-organizational problem solving of emergencies and extreme events are complex research fields where scarce experimental data is available. To address this problem, the Inter-GAP In Vivo System, was developed to run behavioural experiments of complex crisis. The system design and testing included three categories of participants: for pilot testing, first year university students; for theoretical validity, college students engaged in emergency management programs; and for field validity, expert decision makers who managed major crises. A comparative assessment was performed to select the most suitable video conferencing software commercially available, since it was more cost-efficient to acquire a tool already developed and customized it to the experiment needs than it was to design a new one. Software features analyzed were: ease of use, recording capabilities, format delivery options and security. The Inter-GAP In Vivo System setup was implemented on the video conference platform selected. The system performance was evaluated at three levels: technical setup, task design and work flow processes. The actual experimentation showed that the conferencing software is a versatile tool to enhance collaboration between stakeholders from different organizations, due to the audiovisual contact participants can establish, where non verbal cues can be interchanged along the problem solving processes. Potential future system applications include: collaborative and cross – functional training between organizations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".