Canadian Approach to a Diverse and Inclusive Workplace for Wildland Fire Management
Bibliographic record
Abstract
Aging demographics in the Canadian Wildland Fire workforce and additional challenges around recruitment and retention of skilled workers point to the need for efforts that encourage diverse, inclusive, and healthy workplaces. The objective of this presentation is to share the results of a Canadian Wildland Fire demographic survey that will be conducted in the summer of 2019 and highlight how the Canadian Wildland Fire community is seeking to approach the issue of diversity and inclusion. The survey will be sent to permanent and seasonal wildfire staff across Canada to gather information on demographics and barriers to recruitment and retention. It is likely that the results of the survey will resonate with the global wildland fire community. Therefore, we will be sharing ideas from Canada on how we hope to approach the issue followed by a facilitated discussion with the audience to gain insight from a variety of jurisdictions so that we can leave with a better inderstanding of the value of a diverse and healthy workforce and how we may get there as a wildland fire community.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".