Learning Poker in Different Communities of Practice: A Qualitative Analysis of Poker Players’ Learning Processes and the Norms in Different Learning Communities
Bibliographic record
Abstract
Several studies have focused on some of the skill elements needed to become a successful poker player, but few have described the poker players’ learning processes. No studies have used a learning theory to analyse poker players’ variety of learning methods or analysed whether the competitive and deceptive nature of the poker game have an impact on the players’ learning outcome. This article examines 15 poker players’ learning processes and how the players enter different learning communities of practice, arguing that different communities have different norms. In a friendly community of practice, the players were generous in helping each other and revealed secrets so that the group could grow together. In the competitive community of practice, the players were more cautious, and misleading information was common. Online poker, as well as new technology, has made several new artefacts (learning tools) available for poker players, and their main contribution is to reveal information that was previously unavailable. Because poker is a game of information, it greatly affects the players’ learning potential. RésuméPlusieurs études ont mis l’accent sur certaines compétences nécessaires pour devenir un joueur de poker performant, mais peu ont décrit les processus d’apprentissage suivis par les joueurs de poker. Aucune étude n’a utilisé de théorie de l’apprentissage pour analyser la diversité des méthodes d’apprentissage des joueurs de poker ni pour déterminer si la nature compétitive et illusoire du jeu de poker a une incidence sur les résultats d’apprentissage des joueurs. Cet article examine les processus d’apprentissage de 15 joueurs de poker et leur entrée dans différentes communautés de pratiques d’apprentissage. On y explique que différentes communautés possèdent différentes normes. Dans une communauté de pratique conviviale, les joueurs s’entraident et révèlent des secrets afin que le groupe puisse grandir ensemble. Dans une communauté de pratique compétitive, les joueurs sont sur leur garde, et les informations trompeuses sont monnaie courante. Le poker en ligne ainsi que les nouvelles technologies ont mis plusieurs nouveaux outils d’apprentissage à la disposition des joueurs de poker. Leur principale contribution est de révéler des informations qui, auparavant, n’étaient pas disponibles. Étant donné que le poker est un jeu d’information, le potentiel d’apprentissage des joueurs en est grandement affecté.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".