Analysing a private city being built from scratch through a social and environmental justice framework: A research agenda
Bibliographic record
Abstract
A growing body of scholarship examines new cities being built from scratch that are developed and governed by the private sector. While this scholarship explores discourse and rhetoric, economic objectives, and some social and environmental impacts of new private cities, scholars to date have not taken a social or environmental justice approach to analysing new city projects. In this article we examine Forest City, a private city project being built on artificial islands off the coast of Malaysia by one of China's largest property development companies, and its unique governance and claims to being 'eco', despite the significant environmental damage it has caused. Intended as a lush and exclusive gated enclave for Chinese nationals, Forest City is a productive case study through which to consider the consequences of a private city using the frameworks of social and environmental justice. We suggest more critical research that engages with social and environmental justice is needed on the many emerging projects branded as eco-cities of the future, a troubling claim that signals a growing normalisation of mega-scale privatisation and loose or absent regulations regarding social inclusivity and environmental protection.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.053 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".