Pyrogeography, historical ecology, and the human dimensions of fire regimes
Bibliographic record
Abstract
Abstract In our 2011 synthesis (Bowman et al., Journal of Biogeography, 2011, 38, 2223–2236), we argued for a holistic approach to human issues in fire science that we term ‘pyrogeography’. Coughlan & Petty (Journal of Biogeography, 2013, 40, 1010–1012) critiqued our paper on the grounds that our ‘pyric phase’ model was built on outdated views of cultural development, claiming we developed it to be the unifying explanatory framework for all human–fire sciences. Rather, they suggest that ‘historical ecology’ could provide such a framework. We used the ‘pyric transition’ for multiple purposes but did not offer it as an exclusive explanatory framework for pyrogeography. Although ‘historical ecology’ is one of many useful approaches to studying human–fire relationships, scholars should also look to political and evolutionary ecology, ecosystems and complexity theories, as well as empirical generalizations to build an interdisciplinary fire science that incorporates human, ecological and biophysical dimensions of fire regimes.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".