A healthy state? Geopolitics, health, and safety: A keynote presentation for the European Conference of Law Enforcement and Public Health
Bibliographic record
Abstract
Law enforcement and public health agencies operate in the context of their national government policies and resources. But improving and protecting health and community safety are made more difficult by four dark geopolitical forces which have been accelerating in recent years. Neoliberalism is driving governments to pursue low tax, low regulation small-state policies and aiding the expansion of uncontrolled globalization and colonialization of health-damaging manufacturing and services by multinational companies. The “Sovereign individuals,” first described in 1997, are the super-rich who have mastery over information technologies to avoid taxes and hide their wealth from governments. The vast offshoring of wealth has led to increasing inequalities in wealth and health, between rich and poor, further adding to civil distrust and unrest. Loss of revenues prevents government funding of health, welfare, and public protection whilst creating greater need for it. To explain the increasing poverty and harshness of life for the masses, there has been a rebirth and rise of populism. This has accelerated political corruption, created culture wars, fomented distrust of others, and added to global political instability. The information revolution has influenced all of these: it has created its own dark geopolitical force through the explosion of social media and industrial disinformation undermining individual critical thinking and democratic processes. Law enforcement and public health agencies face the consequences of these dark forces in their daily work, but they also need to understand more and develop more effective partnership responses to counter the worst excesses of the new geopolitical realities.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.032 | 0.026 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".