Plucking the ‘golden goose’, alive: The impacts of ‘supercity’ governance on a small island community
Bibliographic record
Abstract
KNOWS BEST?The myriad lessons for our nation's central and local governments from 2023's devastating Auckland Anniversary weekend floods and Cyclone Gabrielle were quickly acknowledged following those events, most of them focused on a chronic failure to upgrade infrastructure to meet climate change impacts.What also became apparent in the immediate aftermath of Gabrielle was that the communities worst affected were mainly small communities subject to the governance of city-based local governments.Moreover, what saved those communities from worse outcomes was, by all accounts, not support from central or local governments; it was the collective wisdom, connectedness, local resources and community spirit, built over decades and generations, that are endemic to those communities.This commentary was conceived before the disastrous events of early 2023, but is the more important in the wake of those experiences and what the country has learned from them.The authors are members of a research-based group, Project Forever Waiheke, which was established to lobby for management of tourism on a small island where both the community and the natural environment were being seriously eroded by overtourism that was being aggressively promoted by both tourism operators and the governing body, Auckland Council.Accordingly, this commentary is focused on the Waiheke Island community that we live in, love and know intimately.However, the key points we wish to make-about the need for greater localised control over planning for unique places and spaces-apply to small diverse communities all over New Zealand.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.035 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".