Recent research in the socio-cultural domain of gaming and gambling : an annotated bibliography and critical overview
Bibliographic record
Abstract
The purpose of this overview is to systematically identify and critically analyze the relevant scientific, descriptive, and policy-oriented literature in this area with the aim of providing a resource that will inform future research and development in gaming and gambling studies. Accordingly, this review constitutes a source document on gaming and gambling studies produced in the latter part of the twentieth century in English- and French-speaking countries. Studies are included that examine the distribution and patterning of gaming and gambling among population sub-groups; social structural factors influencing those patterns within the context of traditional and emerging norms, values and beliefs; and social impacts of gaming and gambling. Literature produced between 1980 and 2000 in North America, Europe, and non-European Commonwealth countries is included, as well as (in the critical overview) a summary of gaming and gambling among Blackfoot peoples, as recorded in ethnographic studies available through the electronic version of the Human Relations Area Files (eHRAF). A range of studies representing different methods and disciplines were included as this material was found in both published and unpublished (“grey literature”) forms. Materials were included if they were judged by the project team to comprise a significant contribution to the literature in this domain.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".