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
The evaluation's findings were clear: Work in both countries was decisive or important in achieving a number of key outcomes.In Canada, Pew's project contributed to placing more than 150 million acres into protected status and in securing passage of two landmark provincial agreements that set targets to protect or sustainably develop another 400 million acres. With the addition of lands that could be protected through the campaign's timber industry initiatives, the Canadian work affects about 700 million acres of land that is either currently protected, that governments have pledged to protect, or that may be subject to restrictions on commercial and industrial development.In Australia, Pew's efforts contributed to protecting about 75 million acres in the Outback, through a mix of conservation reserves, Indigenous Protected Areas, and land purchases. The evaluators also recognized the project's role in obtaining over half a billion dollars to support Indigenous conservation programs in the Outback. In both countries, the evaluation attributed campaign successes to a combination of well-executed tactics, including leveraging science-based arguments for the value of land conservation, empowering Indigenous communities to assert their rights over native lands, and cultivating strong relationships with key decision-makers from across the political spectrum.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it