Bibliographic record
Abstract
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.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.266 | 0.099 |
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.
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".