Bibliographic record
Abstract
June 2005 was to bring online gambling out of the shadows and into the spotlight. PartyGaming, a start-up formed in 1997, launched a flotation (Initial Public Offering) on the London Stock Exchange that valued the firm at £4.64 billion giving it a larger market capitalisation than British Airways. PartyGaming had become the dominant player in the booming online poker market with its PartyPoker brand having over 50% market share. However, this float – as with Internet gambling in general – was not without controversy. While PartyGaming had an online gambling license from the tax haven of Gibraltar, nearly 90% of its revenue came from the United States, where the authorities viewed Internet gambling as illegal and threatened legal action. The complex operations of this truly global firm with bases in London, India, Gibraltar and Canada, the background of its founder Ruth Parasol in Internet pornography and the handling of its flotation also raised concerns from an ethical perspective, with some commentators questioning whether the float should have been allowed at all. These concerns were then confirmed as US legislation to curb online gambling was passed in September 2006, leading to PartyGaming's exit from the US market and an immediate fall of 58% in the share price. This case study analyses the entrepreneurs behind PartyGaming, its growth, the challenges it has faced, the ethical issues it poses and its future prospects. The case draws on theory from e-commerce, strategy and ethics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".