MétaCan
Menu
← Back to cohort
Record W3216888424 · doi:10.4324/9781003034445-10

The sporting mythscapes of Aotearoa New Zealand

2021· book-chapter· en· W3216888424 on OpenAlexaboutno aff
Mark Falcous, Sebastian Potgieter

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAotearoaHistoryGeographyOceanographySociologyGeologyGender studies

Abstract

fetched live from OpenAlex

This chapter explores the politics of remembrance that surround representations of the sporting past in New Zealand. We approach the telling of the sporting past as a contested terrain with varying, selective and at times competing versions of it circulating within the national imagination. These circulate within literature (academic and popular), museums, commercial culture (cereal boxes, TV advertising), and memorials such as statues. Specifically, certain sports, events, figures and achievements are remembered, memorialised, highlighted and celebrated. Others meanwhile are forgotten, ignored or obscured. In this regard, there are questions around whose versions of the sporting past predominate within the national imagination; the answer to which demonstrates that remembering the sporting past is entangled with the broader cultural politics within which sport is imbricated. We explore these issues by looking at three examples from popular histories of sport and memorialisation: the 1976 Montreal Olympic boycott; the 1981 Springbok tour; and the 1907–1908 All Golds, to reveal the deeper significance and entanglements of representations of the sporting past.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.366
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.019
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.035
GPT teacher head0.297
Teacher spread0.262 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicSport and Mega-Event Impacts→French-language works237,207→