Let My People Go: Human Capital Investment and Community Capacity Building Via Meta/Regulation in a Deliberative Democracy - a Modest Contribution for Criminal Law and Restorative Justice
Bibliographic record
Abstract
Globalization and the new information economy are putting great stress on western high-wage economies of which Canada is an exemplar. As individuals and together as a society, Canadians are being forced to become more flexible and strategic in adjusting to changing employment opportunities and economic challenges. Meanwhile, governments have shifted from being purveyors of welfare to being supervisors of both markets and decentralized/ privatized public services. Key roles for the government in this new political environment are the sponsorship of mechanisms for autonomous, individual human capital investment as well as for community responses to these emerging economic and social challenges. This new supervisory state governs by various forms of regulation which are often developed through participatory processes. From legislative rulemaking to community consultation, governance can take the form of a broad and multi-faceted deliberative democracy. Responsive regulation is even having an impact on criminal justice, often thought to be one of the most inflexible arenas of state activity, primarily, though not exclusively, through what is called "restorative justice." True restorative justice in response to crime has characteristics of deliberative democracy that have the potential to make a modest, if not significant, contribution to human capital development and community capacity building. The story of these hopeful developments is the subject of this article but, just as the devil is often said to be in the details, close analysis of detail can be the source of things divine in the best of all possible worlds. The reader is, therefore, forewarned that there follows a highly condensed discussion of the relations among models of criminal justice, regulatory theory, deliberative democracy and human/social capital investment. But the ultimate message is simple: we have the social, economic, political, and indeed legal means to liberate people's creative and productive capacities in multiple ways and in curious places; hence, the reference in the title to the traditional black spiritual "Let My People Go."
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".