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Record W2013610226 · doi:10.4309/jgi.2005.14.12

Can playing poker be good for you? Poker as a transferable skill

2005· article· en· W2013610226 on OpenAlexvenueno aff
Adrian Parke, Mark D. Griffiths, Jonathan Parke

Bibliographic record

VenueJournal of Gambling Issues · 2005
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAdvertisingBusiness

Abstract

fetched live from OpenAlex

Much of poker's appeal is due to the fact that, unlike many other forms of gambling, the scope to influence the outcome is vast. Put simply, it is primarily a game of skill. Although some of the necessary skills can be inherent (such as emotional intelligence, i.e., the ability to process emotional information, particularly as it involves the perception, assimilation, understanding, and management of emotion), many of the more idiosyncratic skills are only acquired through experience. As a result, successful poker players will always seek to improve by being critical of their own play and assimilating a behavioural repertoire of opponents' playing styles. Some sense of humility is required.

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)0.003
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 score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.005
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0020.003
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 head0.213
GPT teacher head0.452
Teacher spread0.239 · 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

Citations29
Published2005
Admission routes1
Has abstractyes

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