World scientists’ warning: The behavioural crisis driving ecological overshoot
Bibliographic record
Abstract
Previously, anthropogenic ecological overshoot has been identified as a fundamental cause of the myriad symptoms we see around the globe today from biodiversity loss and ocean acidification to the disturbing rise in novel entities and climate change. In the present paper, we have examined this more deeply, and explore the behavioural drivers of overshoot, providing evidence that overshoot is itself a symptom of a deeper, more subversive modern crisis of human behaviour. We work to name and frame this crisis as 'the Human Behavioural Crisis' and propose the crisis be recognised globally as a critical intervention point for tackling ecological overshoot. We demonstrate how current interventions are largely physical, resource intensive, slow-moving and focused on addressing the symptoms of ecological overshoot (such as climate change) rather than the distal cause (maladaptive behaviours). We argue that even in the best-case scenarios, symptom-level interventions are unlikely to avoid catastrophe or achieve more than ephemeral progress. We explore three drivers of the behavioural crisis in depth: economic growth; marketing; and pronatalism. These three drivers directly impact the three 'levers' of overshoot: consumption, waste and population. We demonstrate how the maladaptive behaviours of overshoot stemming from these three drivers have been catalysed and perpetuated by the intentional exploitation of previously adaptive human impulses. In the final sections of this paper, we propose an interdisciplinary emergency response to the behavioural crisis by, amongst other things, the shifting of social norms relating to reproduction, consumption and waste. We seek to highlight a critical disconnect that is an ongoing societal gulf in communication between those that know such as scientists working within limits to growth, and those members of the citizenry, largely influenced by social scientists and industry, that must act.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".