Bibliographic record
Abstract
Galvanised by the increasing complexity in managing incidents of mass violence and mass casualty, the emergency response agencies in Calgary, Canada identified the need to develop research-based policies, establish common strategies/ tactics and conduct more joint training across all hazards. By identifying the challenges with initial command, coordination and control activities at scene, the Calgary first-responder community designed and implemented an integrated training programme to support interoperability between front-line incident commanders and supervisors. The training programme was created to address the differences in each respective agency's policies, procedures and cultures that can be barriers to integrating into a single incident management structure or unified command. Using features of interoperability like shared situational awareness and joint risk assessment, and applying the concepts into tactics like rescue taskforce, the training is building critical command relationships for the future. This training has further expanded into a programme with joint policy and procedure development, incident debriefings, expanded exercises, and tactic specific training. This paper describes how the members of Calgary's first-responder community are stepping beyond their silos of excellence and unifying their planning, preparedness and response programme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".