Bibliographic record
Abstract
The 2030 Agenda for Sustainable Development was adopted by all member states of the United Nations (UN) in 2015. One year later, Habitat III, the first UN global summit to adopt the sustainable development agenda, took place in Quito, Ecuador. Habitat III served as a forum for discussing the planning and management of human settlements for promoting sustainability. Global stakeholders are increasingly acknowledging that Agenda 2030 must embrace people-centred approaches to address the interconnectivity of today’s challenges in order to deliver its transformative promise to human settlements. To this end, human safety and security, which is concerned with whether people live in conflict or peace, provides an effective programming framework for promoting inclusive and sustainable human settlements. This paper explores the nexus between human security and the sustainable development of human settlements. Drawing on a broad range of literature, the paper begins by considering the conceptual basis of sustainable development through the lens of inclusivity. This is followed by a detailed explanation of why human security is central to promoting the sustainability of settlements. The paper also offers some insight into measuring and modelling human security for the purpose of sustainable settlement programming. The paper concludes by offering some thoughts about why statutory public safety stakeholders should work with communities and civil society in order to secure and sustain positive gains for human settlements.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".