Bibliographic record
Abstract
Abstract Consumer policy is already being shaped by a combination of governance models. This position paper argues that complexity‐oriented convergence models are a timely addition. Modern day consumer policy is characterized as interactive and integrative, replete with shifting boundaries and coalitions and evolving roles for each of state, market and society. This paper focused on governance in the consumer policy arena, arguing that this process needs to acknowledge and reconcile complexity. After describing the basic tenets of complexity theory, two characteristics of contemporary tri‐sector interaction (i.e., sector blurring and sector distortion) were discussed. These boundary characteristics necessitate the need for approaches that can accommodate complexity during consumer policy governance. Three examples of the latter were profiled: sector convergence, network governance and cross‐sector governance. These conceptualizations accommodate the dynamics, complexity and emergence of contemporary consumer policy governance.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".