Gamblers, grinders, and mavericks: The use of membership categorisation to manage identity by professional poker players
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Historically, gambling has varied considerably regarding its moral and social meanings. Whilst frequent gambling is often constructed as deviant, professional poker playing can be argued to occupy the conflicting position of both deviant and legitimate. This study explored how professional poker players negotiate this potentially troubled aspect of their identities. Semistructured interviews were conducted with four men from the United Kingdom who played casino poker. The data were analysed using membership categorization analysis. The following membership categorisations were in use within participants' accounts: gambler, grinder, maverick, and nongambler, as well as the central categorisation of professional poker player. Participants constructed themselves as stigmatised because they were frequent gamblers and poker players. Thus professional poker players utilised membership categorisation to distance themselves from other membership categories, particularly gamblers, which was achieved primarily through claims warranted by reference to skill and control.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it