Perspectives on metropolitan governance
Bibliographic record
Abstract
To complement Zack Taylor’s paper on Regionalism from Above: Metro Governance in Canada, the journal commissioned four short ‘perspectives’ from Commonwealth countries grappling with similar issues – Australia, England, New Zealand and South Africa. The purpose was not in any way to ‘review’ Taylor’s work, but rather to establish a broader picture of issues and trends in metropolitan governance, and to identify common threads. The perspectives from Australia, England and South Africa focus on recent developments and governance issues in particular metropolitan areas. These are respectively the fast-growing outer metropolitan sub-region of Western Sydney; the long-established conurbation of Greater Manchester; and the vast, emerging ‘multi-nodal sprawl’ of South Africa’s Gauteng City Region, centred on Johannesburg. The New Zealand perspective takes a different approach, exploring the implications of shifts in national policy towards a focus on wellbeing and the quality of life in communities, with significant implications for the future of local government and the way metropolitan areas are governed. Nevertheless, all four perspectives reveal similar underlying concerns that metropolitan governance frameworks and practices often struggle to keep pace with global trends, urban growth, community needs and national priorities. Effective inter-government relations are crucial, but local governments may not be at the table, or their views may be largely ignored. The governance of metropolitan regions becomes increasingly fraught, a battleground between the forces of devolution and centralisation. How can meaningful and effective collaborative governance be realised? Who should take the lead and do we have the right tools and skills? In such a complex and fluid environment, can we realistically expect anything more than brief periods of clarity and consensus that at least enable agreement on the next few steps?
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".