Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments both in Australia and abroad are showing increasing interest in facilitating growth in the adaptation services market to help communities to prepare for and respond to the impacts of climate change. This review appraises evidence of the effectiveness and efficiency of these markets and the role that governments play in their establishment and operation. We found that the majority of empirical work on climate service markets concentrates on demand related aspects, such as user preferences, and less on the supply and policy aspects of the market. We propose that this stems from an assumption that by increasing demand, suppliers will follow. As climate service markets are generally policy-based imperatives, they do not emerge according to conventional market rules, and act more like a quasi-market or public service market. We suggest that, due to the normative goals of climate service markets to aid climate change adaptation, governments would do well to steward these markets into more robust systems. We conclude by recalling that the exchange of climate service information is not limited to market arrangements, and that government’s choice to use markets to help exchange climate service data is another example of the legacy of new public management paradigms as we shift into a new public governance era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.000 | 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) | 0.001 | 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 it