Bibliographic record
Abstract
Hamilton has more than 200 parks and reserves. They come in all shapes and sizes, and are used for everything from walking dogs to playing sport, from sitting in the sun to skateboarding. Some link to the city’s extensive gully system, one encircles the lake, others open out to the Waikato River. Altogether, the city’s reserve land covers more than 1000 hectares. \n \nThe parks help tell the history of the area, from Miropiko and Lake Rotoroa, which have been important to Ngaati Wairere since pre-European days, to Steele Park, which was the first in the new settlement of Hamilton and was created in 1868 as Sydney Square. \n \nAnd as the city spreads outwards, parks continue to be dotted among the suburbs, including the newest of the new such as Moonlight Drive Reserve in Rototuna, which comes complete with playground and newly planted trees. It is 12km from Steele Park, was developed more than 140 years later, and is about quarter of the size. The grid of the original settlement has been lost in the intervening decades, so Moonlight Drive Reserve is not quite the perfect square that Steele Park is. Both, however, offer recreation spaces and meeting points for locals. \n \nThe parks are the city’s breathing spaces, connecting residents to their neighbourhoods and providing free recreation for anyone who wishes to use them. Take a stroll as Wintec’s School of Media Arts students, with the help of Hamilton City Council, tell the story of our parks and the people who use them.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.314 | 0.099 |
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".