Stigmatization Dialogue: Deconstruction and Content Analysis
Bibliographic record
Abstract
The present study examines the use of clinical rhetoric and discourse within the professional online forum "Gambling Issues International." The aim of this research is two-fold: (a) to examine the discourse of clinicians and researchers in defining gambling pathology; and (b) to investigate how professionals perceive the potential problems of stigmatization for their clients. Both qualitative and quantitative methodologies are employed in analyzing the data. Computer-based content analysis of listserv members' records is used to examine professionals' discussion of client and societal responsibility for gambling addiction, the diagnostic categorization of individuals, and the consequences of stigmatization. Quantitative methodologies utilize an online survey that is distributed to listserv members. The survey assesses how the medical model influences the discourse of researchers and clinicians in the field of problem gambling. The results of this research will contribute to an improved understanding of how the medical model creates such discourse and its role as a factor contributing to the stigmatization of clients.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".