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Record W4231240694 · doi:10.7765/9781526107039.00006

Preface

2016· book-chapter· en· W4231240694 on OpenAlexaboutno aff
Brad Millington, Brian C. Wilson

Bibliographic record

VenueManchester University Press eBooks · 2016
Typebook-chapter
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityLabor market segmentationSocial inequalityContext (archaeology)Production (economics)Work (physics)State (computer science)TributeQuality (philosophy)Political sciencePrecarious workSociologyLabour economicsEconomicsUnemploymentEconomic growthGeographyEngineeringLaw

Abstract

fetched live from OpenAlex

We golfBrian grew up a few blocks from a nine-hole golf course in a small city in Ontario, Canada -and spent many an afternoon, evening, and sometimes night (i.e.junior hours) playing rounds, chipping, and putting on greens long-abandoned by those who had completed their rounds.Brian vaguely remembers friends who joked that you should "not lick your golf balls" because they would be coated with the chemicals that were sprayed on the course.Brad played golf in the Ontario suburbs as much as possible too.Golf was and remains a social activity as much as a sporting endeavour -a reason to spend time with family and friends.The site of lush greens and fairways was a sign that winter had thawed.We both grew up enamoured with the pristine courses we saw on television.Brian's parents took him to see the Canadian Open golf tournament at Glen Abbey Golf Club in Oakville, Ontario in the early 1980s, where he saw Jack Nicklaus, Greg Norman and others manoeuvre a golf course that Brian remembers as a utopian landscape -with fairways that looked like putting greens, and putting greens that looked like billiard tables.Brad's memories of golf are tied as much to watching tournaments such as the Masters on TV as they are to experiences actually playing the game.Golf is a game we both grew up with, and one we genuinely enjoy and appreciate.We have golfed together many times.We tell you this at the outset because this book, and the research and arguments that are featured within it, unsettled how we see the game of golf.Specifically, this work raised serious questions for us about golf 's evolving relationship with the environment, and how various stakeholders in golf have responded to pressing environmental concerns.By raising these questions in the pages that follow, we might be seen by some as social scientists (i.e.critics) who don't like and don't 'know' golf.This couldn't be further from the truth.However, as social scientists who are ultimately interested in and concerned with the factors that influence those who make decisions that have important consequences for public health and the health of the world's natural environments we feel obligated to tell the story that emerged from our research into golf 's environmental history -and its environmental present.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.081
GPT teacher head0.309
Teacher spread0.227 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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