Bibliographic record
Abstract
Borch, A. (2015). Problem gambling—a Lacanian Real. The International Journal Of Alcohol And Drug Research, 4(1), 71-76. doi:http://dx.doi.org/10.7895/ijadr.v4i1.197The concept of addiction has been criticized for being mainly based on self-reporting in therapeutic and research settings, and that it is functional for people in these settings to report that they are addicted—driven by forces beyond their capacity to control. In this paper, I take this criticism seriously into account and argue that problem gambling belongs to the Lacanian Real, in short, referring to those parts of our existence that might be sensed and even acknowledged, but that never can be wholly grasped. Based on qualitative research of households with reported gambling problems, I argue that neither problem gamblers nor their spouses seem to know why the person gambles and why he or she keeps on gambling even though s/he knows it is damaging. The unknown and incomprehensible aspects of problem gambling (the Real) tend, as part of the gambler’s process of ‘recovering,’ to be repressed and replaced with the concept of addiction. This repression mechanism is observed in other contexts as well, not least in scientific milieux studying gambling, and reflects interests and power in society. Exploring the addiction concept from a critical point of view is necessary to sort truth from myth and make scientific enhancements.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".