Predictive rebound & technologies of engagement: science, technology, and communities in wildfire management
Bibliographic record
Abstract
ABSTRACT Technologies of anticipation – such as predictive analytics, forecasting, and modelling – offer appealing promises to those governing risk. While previous work has challenged notions that such technologies are value neutral, we must attend to specific – and subtle – ways that values are embedded and manipulated within these systems. Using the case of wildfire management, I propose the concept of predictive rebound to highlight two challenges: (1) that increasingly accurate predictive models do not always translate into the initially intended real-world gains, but rather can end up being applied to alternative ends and (2) that perceived accuracy of predictive models can be misunderstood as reducing the need for explicit debate about values within decision-making. Further analysis of predictive rebound in real-world contexts will help to inform more effective engagement with stakeholders about values, priorities, and risks; revealing situations where technologies of anticipation obfuscate value-laden decisions and facilitate unintentional drift in management priorities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".