Bibliographic record
Abstract
The corporations of our future will be whatever we can collectively imagine and work together to make a reality. Dialogic law and regulation is a generative tool that can build the bridge between the present and an imagined future. Regulators keep people on the bridge by identifying the kinds of dialogues we want corporate actors to have and by encouraging, coaching, and sometimes assisting them to have those dialogues. This approach works because small changes in the way corporate actors talk to and interact with each other can have dramatic effects on the emergent corporate culture. This thesis develops and tests a theory of Dialogic Regulation. The theory assumes that corporate law and regulation is about attaining or maintaining a desired corporate behaviour, the best way to change behaviour is to learn a new one, and learning is a social process that involves dialogue. The model was tested using an experimental game where the rules of the game were treated as proxies for the “law” and the authority figure directing the experiment was treated as a proxy for the corporate “regulator”. The game was called the “Pay-Off” game. Half-way through the game the rules were changed using one of three different regulatory techniques: 1) Rules: a simple rule change, 2) Audit: a rule change combined with an audit and punishment procedure for infractions, and 3) Dialogic: a rule change combined with a dialogic intervention about the rules. Participants were tested not only for their behavioural reactions to the interventions (Compliance to the rules) but also to determine if they learned anything about the rules (Adherence to the rules). The games experiment showed that for simply behavioural outcomes the Audit Based Regulation approach was the most effective. The experiment also showed that there is significant promise in a Dialogic Regulation approach if the regulatory desire is to have participants learn. While Dialogic Regulation shows promise, a lot more work needs to be done to refine the application of the theory before it is used in real-life regulatory settings.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".