MétaCan
Menu
Back to cohort
Record W2915225699 · doi:10.23912/9781911635062-3977

Introduction to Clubs

2018· book-chapter· en· W2915225699 on OpenAlexaboutno aff
Clayton W. Barrows, Michael Robinson

Bibliographic record

VenueGoodfellow Publishers eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsReputationClubPolitical sciencePleasureLaw

Abstract

fetched live from OpenAlex

Private clubs have existed for as long as people have desired to gather in groups to do things together. It has been suggested that private clubs (and their predecessors) date to the Roman baths but probably pre-date even those. It is doubtful that the Roman baths represented the first time people congregated in groups to socialize, discuss commerce, politics, or just engage in a mutually agreeable activity. Certainly, most agree that the ‘modern’ clubs (in the English speaking world) originated in England, were limited to ‘gentlemen’ and organized for social, political, business and/or pleasure reasons. The concept was then ‘exported’ along with ex-patriots all around the world. Clubs have since evolved to the point where they exist in countries around the world although they are embraced to a greater or lesser extent in different places. Examples of private clubs can be found in such countries as England (and the greater UK), Ireland, the United States, Canada, Australia and New Zealand, South Africa, Switzerland, Hong Kong, India, Pakistan, Japan, Singapore, and the UAE. Perhaps no country has adopted the idea of clubs as much as the USA, where they have evolved into a veritable industry, are protected by law, and number into the thousands. Humans, being social creatures, long to spend quality time with others – ‘others’, historically, representing those of their own kind. Perhaps it is for this reason that clubs have, rightly or wrongly, developed a reputation for being discriminatory. People generally find benefits from spending time with others. These benefits may accrue in many forms, including personal, professional, and political.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.266
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0060.004
Scholarly communication0.0100.006
Open science0.0020.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.2660.099

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.

Opus teacher head0.025
GPT teacher head0.268
Teacher spread0.244 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueGoodfellow Publishers eBooksSame topicNonprofit Sector and VolunteeringFrench-language works237,207