Bibliographic record
Abstract
abstract: Ice hockey is a minority sport in New Zealand, but many people are dedicating their lives to grow its popularity in the country. Hockey in Kiwi Land: Exploring the Ice Hockey Culture of New Zealand presents the voices of those involved in the country’s largest city, Auckland, and their efforts in the country’s highest league. The New Zealand Ice Hockey League is made up of people of different backgrounds, including fathers, teenagers, university students, full-time workers, and Canadians. Information on ice hockey’s culture was found through spending a week in Auckland and interviewing different people involved with the West Auckland Admirals, the defending champion at the time. The information was then created into a website that displays both a written and visual component. The photo stories were made to capture the physical aspect of a game that wants to dominate in a country obsessed with rugby. The interviews capture why those born in New Zealand love ice hockey and what needs to change to promote the sport better. Many in the league came from ice hockey haven Canada, and they provided insight on the differences they noticed between New Zealand and North America. The project taught me more about New Zealand’s ice hockey programs and how they differ from those in North America. The interviews showed that while the sport will be a minority in the country for the next few years, it will continue to grow through the joint efforts of international and New Zealand-born players giving back.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".