How bureaucracies interact with Indigenous Fire Stewardship (IFS): a conceptual framework
Bibliographic record
Abstract
Abstract Background Indigenous Fire Stewardship (IFS) is contested within settler-colonial contexts, where its development is shaped by complex and dynamic socio-cultural, legal, and political factors. This manuscript draws from the policy sciences to sketch out a “zone of interaction” between IFS and the state’s wildfire policy system. Drawing from the strategies of bureaucracies, our goal is to illustrate the patterns in this “zone of interaction,” and to identify the implications for IFS, as well as for Indigenous Peoples and landscapes. Results Drawing insights from the Australian and Canadian contexts where governments are restoring lands and reconciling with the laws and governance of Indigenous Peoples, we illustrate how IFS interacts with the state. We do this in two ways. Figure 1 shows that the state has three general strategies for dealing with IFS: avoidance (ignoring IFS), coping strategies (carefully considering and sometimes accommodating IFS), and learning (embracing and accommodating IFS). We document that post-wildfire, there are affective drivers that move the state’s approach from avoidance to learning ; however, over time, as public attention shifts away from alternatives, the strategy moves back to either avoidance or coping strategies (where the state is required to engage with IFS, but cannot fully embrace it because of institutional, tenure, or jurisdictional issues, among other constraints). Figure 2 documents the six coping strategies available to bureaucracies in dealing with IFS, which either institutionalize , partially institutionalize , or do not institutionalize IFS. Each of these pathways has implications for IFS, and the manuscript details the effects on IFS practices, and the impacts for people and landscapes. Conclusions To better support IFS, we must look beyond the institutionalization of IFS within the state, and nest IFS within Indigenous laws and governance. An Indigenous-led IFS approach can operate in parallel with the state, and develop innovative land-access arrangements and Tribal Parks to apply IFS to landscapes. New structures of engagement must be designed for this parallel space, grounded in the principle of free prior and informed consent (FPIC), and with explicit focus on deconstructing power differences.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".