Bibliographic record
Abstract
Confronted with mounting challenges such as rapid urbanization, global living disparities, climate change, natural disasters, the recent upheaval of the COVID-19 pandemic, and the Ukrainian-Russian War, the United Nations and other international entities have redirected their focus towards fostering resilient, democratic, cohesive, sustainable, and digital societies. This paradigm shift necessitates a global partnership to conserve, protect, and restore the health and integrity of the Earth's ecosystem. Amid these complex issues, the Ekistics principles provide a comprehensive framework, advocating for harmonious urbanization and balanced territorial development. These principles prioritize the incorporation of ethical, social, environmental, and economic considerations into urban planning and governance processes. Nevertheless, critics argue that sustaining the capitalist system poses a challenge to achieving harmonious settlements, balanced urbanization, and the preservation of natural, environmental, and cultural values. This paper underscores the significance and relevance of Ekistics principles in shaping the discourse around Sustainable Development Goals (SDGs). It contends that revolutionary changes in globalization, communications technology, and industrial development mandate a reevaluation of existing approaches to address the most pressing global issues and their potential solutions. By highlighting the Ekistics principles, this article contributes to a nuanced understanding of the intricate dynamics shaping urban living in the contemporary era. The analysis aims to stimulate a reorientation of prevailing strategies and policies, fostering a more sustainable and harmonious trajectory for global urbanization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".