Institutional Experimentation, Directed Devolution and the Search for Policy Innovation
Bibliographic record
Abstract
One response to the employer’s search for “flexibility” (most evident in the “platform economy”) may be “institutional experimentation,” i.e., changes to institutions and how they relate to organizations and labour standards. Our question: “What form of institutional arrangement can best enable the lessons of policy experimentation to be learned and disseminated?” Under directed devolution, as proposed here, legal entitlements or obligations would be set at a higher level (say, a national jurisdiction). A lower level (“subsidiary bodies”) would be required to work out detailed implementation of those standards, with a view to protecting the affected workers’ interests. The subsidiary bodies might cover specific industries or groups of industries. They may need to be quite innovative. Results would be evaluated and ideas generated. By emphasizing flexibility and learning, directed devolution enables actors to learn from the experiments of other actors. One such example is the regulation of New York’s road passenger transport industry in 2019, a highly innovative attempt to convert a high-level time-based minimum standard into a practical, local solution. Directed devolution is a form of multi-level policy-making, with some similarities to the concept of subsidiarity, but more tightly integrated. Other relevant but distinct forms of multi-level bargaining include the ILO Conventions , the Bangladesh Accord and several forms of regulation adopted in Australia. Actors and policy-makers should have long-term strategies, be careful in their processes of selecting institutional members, and be prepared to deal with powerful opposition. Directed devolution can be useful wherever establishing enforceable general principles is important and can make a real difference, but there are complications with implementation if circumstances vary considerably among organizations or industries. Devolution can be achieved without losing enforceability, and this can be done without shifting power away from those with less power. Directed devolution is a complement to, not a substitute for, specific regulatory interventions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".