The Human Ecology of Overshoot: Why a Major ‘Population Correction’ Is Inevitable
Bibliographic record
Abstract
Homo sapiens has evolved to reproduce exponentially, expand geographically, and consume all available resources. For most of humanity’s evolutionary history, such expansionist tendencies have been countered by negative feedback. However, the scientific revolution and the use of fossil fuels reduced many forms of negative feedback, enabling us to realize our full potential for exponential growth. This natural capacity is being reinforced by growth-oriented neoliberal economics—nurture complements nature. Problem: the human enterprise is a ‘dissipative structure’ and sub-system of the ecosphere—it can grow and maintain itself only by consuming and dissipating available energy and resources extracted from its host system, the ecosphere, and discharging waste back into its host. The population increase from one to eight billion, and >100-fold expansion of real GWP in just two centuries on a finite planet, has thus propelled modern techno-industrial society into a state of advanced overshoot. We are consuming and polluting the biophysical basis of our own existence. Climate change is the best-known symptom of overshoot, but mainstream ‘solutions’ will actually accelerate climate disruption and worsen overshoot. Humanity is exhibiting the characteristic dynamics of a one-off population boom–bust cycle. The global economy will inevitably contract and humanity will suffer a major population ‘correction’ in this century.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".