Analysis of Staff Retention and Recruitment in Ontario’s Wildland Fire Management System: Current Trends and Implications for the Future
Bibliographic record
Abstract
In 2015, the Aviation, Forest Fire and Emergency Services (AFFES) Branch of the Ontario Ministry of Natural Resources and Forestry (OMNRF) began to address an observed attrition of FireRangers’ collective expertise throughout Ontario that has continued to persist. Using aggregate data from the AFFES, the purpose of this project is to analyze this problem and to develop a prototype Microsoft Excel model that can be used to inform better strategies to recruit and retain more experienced FireRangers. The general hypothesis is that the continual loss of collective expertise of FireRanger staff over the last decade will create problematic ‘pinch points’ when Ontario’s demand for professionals who possess the ability to make complex decisions in the face of increasing wildfire risk will outweigh community needs. The practical value of this analysis and model is threefold: (1) to assess the magnitude of the current retention and recruitment problems faced by the AFFES; (2) to project the efficacy of proposed recommendations and policy changes that may be effective in mitigating the attrition of collective experience levels in the AFFES; (3) to serve as a stepping-stone in providing ongoing and improved predictive modeling to ensure that the human capacity in the AFFES is consistently available to address the evolving wildfire management needs of Ontario. Three recommendations based on the results of this analysis and the model are to (1) simultaneously address retention of Fire Crew Leaders and their feeder groups (e.g., Crew Bosses and Crew Members), (2) enact meaningful changes aimed at all FireRangers, regardless of their positions, and (3) improve intra-provincial data sharing and accessibility in order to enhance the scope of this model to demonstrate the importance of recruiting and retaining a diverse workforce. Ultimately, this model is a tool that could continuously be improved to emphasize the need for quickly and decisively implementing strategies to slow the attrition of expertise. Doing so will enable the AFFES to consistently respond to wildfire in accordance with the values and goals outlined in Ontario’s Wildland Fire Management Strategy into the future.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".