Bibliographic record
Abstract
Since it entered into force in 1994, signatories of the Law of the Sea Convention (LOSC) have been obligated to demonstrate that they can effectively manage the resources within their Exclusive Economic Zones (EEZs). In 1998, the Australian Government took the first step to fulfil its obligation to LOSC and released Australia's Oceans Policy (AOP), a world first policy initiative focussed on providing a framework for integrated ecosystem based management of Australia's vast marine domain. Both Canadian and New Zealand representatives have been encouraged by the Australian Government to observe, and in some instances, take part in, the AOP development and implementation process. Subsequently, both Canadian and New Zealand governments have developed, or are in the process of developing their own ocean policies indicating that some policy components have been transferred from the AOP process. \n\nKey researchers of policy transfer such as Dolowitz (2003); Dolowitz and Marsh (2000); Jones and Newburn (2002); and Evans and Davies (1999) argue that policy transfer processes increase innovation in policy making and allow policy makers, through globalisation and technological advances in communication, to become aware of what other political systems are achieving through policy initiatives. Dolowitz and Marsh (2000: 12) identify eight components of policy that can be transferred. These include policy goals, policy content, policy instruments, policy programs, institutions, ideologies, ideas and attitudes, and negative lessons. Policy transfer has become an important tool for governments as they look for quick solutions to their policy issues. This paper analyses the policy transfer approach and argues that policy transfer is strategically advantageous for states that want to fulfil their domestic and international obligations in ocean 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".