Gambling and corporate social responsibility (CSR): Re-defining industry and state roles on duty of care, host responsibility and risk management
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
Abstract During the 1990s, states embraced legalised gambling as a means of supplementing state revenue. But gaming machines (EGMs, pokies, VLTs, Slots) have become increasingly controversial in countries such as Australia, Canada and New Zealand, which experienced unprecedented roll-out of gaming machines in casino and community settings; alongside revenue windfalls for both governments and the gambling industry. Governments have recognised that gambling results in a range of social and economic harms and, similar to tobacco and alcohol, have introduced public policies predicated on harm minimisation. Yet despite these, gaming losses have continued to climb in most jurisdictions, along with concerns about gambling-related harms. The first part of this article discusses an emerging debate in Ontario Canada, that draws parallels between host responsibility in alcohol and gambling venues. In Canada, where government owns and operates the gaming industry, this debate prompts important questions on the role of the state, duty of care and regulation ‘in the public interest’ and on CSR, host responsibility and consumer protection. This prompts the question: Do governments owe a duty of care to gamblers? The article then discusses three domains of accumulating research evidence to inform questions raised in the Ontario debate: evidence that visible behavioural indicators can be used with high confidence to identify problem gamblers on-site in venues as they gamble; new systems using player tracking and loyalty data that can provide management with high precision identification of problem gamblers and associated risk (for protective interventions); and research on technological design features of new generation gaming products in interaction with players, that shows how EGM machines can be the site for monitoring/protecting players. We then canvass some leading international jurisdictions on gambling policy CSR and consumer protection. In light of this new research, we ask whether the risk of legal liability poses a tipping point for more interventionist public policy responses by both the state and industry. This includes a proactive role for the state in re-regulating the gambling industry/products; instituting new forms of gaming machine product control/protection; and reinforcing corporate social responsibility (CSR) and host responsibility obligations on gambling providers – beyond self-regulatory codes. We argue the ground is shifting, there is new evidence to inform public policy and government regulation and there are new pressures on gambling providers and regulators to avail themselves of the new technology – or risk litigation.
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 | 0.001 | 0.001 |
| 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