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Record W7029116628

Hockey in Kiwi Land

2020· article· en· W7029116628 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArizona State University Library Digital Repository (Arizona State University) · 2020
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsIce hockeyLeagueChampionKiwiPopularityInterview
DOInot available

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.162
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it