Networks of Dis(trust) and Gaming Development in the Philippines: Pagcor and the Entertainment City
Bibliographic record
Abstract
This article analyzes the unique trajectory of the Philippine gaming industry, with a particular focus on the Philippine Amusement and Gaming Corporation (PAGCOR) and the Philippine Charity Sweepstakes Office (PCSO). It aims to provide empirical insight on how state and non-state actors take part in the growing gaming industry in the Philippines in a neoliberal context. This article first addresses the dominant patron-client paradigms, and finds them insufficient to provide an explanation to both the gaming development in the country and the regulatory mechanisms behind this industry. By providing a description of how PAGCOR and the PCSO circumvent dysfunctional bureaucracy and assuage criticism against systemic corruption, this article suggests that a closer look at the complex networks between stakeholders in both the public and private sectors will provide an alternative way of understanding Philippine politics. The strategic decision by the weak Philippine state to invest heavily in the gaming industry presents a clear example of how these complex networks operate. The gaming regulatory policy pragmatically employs current Philippine laws to ensure maximum profit for the state. This article concludes that a critical examination of the gaming industry is necessary, spanning both the legal and illegal types and the social relations of confidence and suspicion between public and private stakeholders.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".