Determinants of Gaps in Human Behaviour in Fire Research
Bibliographic record
Abstract
Abstract This short communication presents the findings of the work conducted by the human behaviour in fire permanent working group of the International Association for Fire Safety Science. Its aim is to identify determinants of research gaps in the field of human behaviour in fire. Two workshops were conducted in 2023 in which research gaps were identified and discussed by twenty experts. The workshops led experts through a series of questions to determine the reasons (or determinants) for these gaps in human behaviour in building fires and wildfires. Through the questions, the primary identified determinants were (1) researchers’ literacy in the variety of methods adopted in the field, (2) difficulties associated with recruitment of study participants, (3) multi-disciplinary barriers across different research sub-domains, and (4) issues in obtaining funding for addressing fundamental human behaviour in fire research questions. Two key issues emerged from an open discussion during the workshops, namely the difficulties in attracting and training new people in the field (given the limited educational offers around the world on the topic) and the need for more regular opportunities for the community to meet.
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 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 | 0.088 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".