Holism, Collective Intelligence, Climate Change and Sustainable Cities
Bibliographic record
Abstract
As the Earth’s systems are under increasing unsustainable pressures, human security is clearly at stake. Cities are regarded to be increasingly important sites for climate responses, and something can still be solved if humankind acts quickly. Novel methods to long-standing quandaries, such as climate change, can now be applied. It is proposed that city adaptation and mitigation strategies should draw on collective intelligence and an innovative holism multi-systemic approach to the encompassing problem of climate change by breaking it up into smaller, manageable problems and crowdsourcing a way out by means of online argumentation systems, computer simulations, and collective decision making tools. As ‘first responders’, cities with similar location or vulnerability characteristics should also be encouraged to transfer best practices between each other. It is futher argued that the enhancements in efficacy and accessibility of big data can be aggregated at a nationwide level in the shape of economic development and sustainability, and in welfare improvements in developing economies. Furthermore, this critical précis argues that whilst adaptation and mitigation strategies are crucial, at the very crux of it, humankind needs a fundamental change of metaphors: from seeing the world as a machine to understanding it as a holistic network.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".