Bibliographic record
Abstract
Abstract. Mathematical models that couple human behaviour to environmental processes can offer valuable insights into how human behaviour affects various types of ecological, climate and epidemiological systems. In many coupled systems, gradual changes to the human system can lead to abrupt tipping points in the overall system, leading to desirable or undesirable new human-environment states. We review aspects of human behaviour–such as social norms and rates of social change–that drive tipping points in the modelling literature, finding that many affect the coupled system depending on the system type and initial conditions. Structural components in the human system, often represented through social networks, are discussed with many studies showing high structural complexity increases the potential for tipping points. Traditional and state-of-the-art techniques in early warning signals are introduced in relation to the human drivers discussed in previous sections. We conclude with an outline of challenges and promising future directions specific to furthering our understanding and informing policy interventions around promoting sustainability within coupled human-environment systems.
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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.035 | 0.029 |
| Insufficient payload (model declined to judge) | 0.067 | 0.056 |
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".