Paleo-data is policy relevant: How do we better incorporate it in policy and decision making?
Bibliographic record
Abstract
The relatively recent acceleration of human activities that adversely impact Earth's systems has led to an increasingly urgent impetus to understand, mitigate, and adapt to these impacts. However, comprehension of natural systems, and fluctuations in their state, requires long-term data to capture the magnitude and direction of changes in these systems over very long time frames (decades to millennia). The current reliance on short instrumental or monitoring time series, that span only the last century or less, is simply inadequate to sustainably manage natural systems. Despite this growing need for long-term information and the abundance of paleo-data available, there has been little effort or success in incorporating paleo-science into policy and decision making. We use examples to demonstrate how paleo-data provides important insights into problems from three different domains: forest management and restoration, water resource management and wetland ecosystem management. We discuss a process through which opportunities to better utilise paleo-data by policy decision makers to achieve better policy outcomes can be identified. This involves first acknowledging the very different characteristics of paleo-scientists and policy makers, followed by recognition of the constraints, or barriers to the uptake of paleo-science information. These barriers exist as much for scientists as for policy makers. Identification of barriers enables opportunities for enhanced collaboration to improve the use of paleo-data for policy and decision making to be identified. Fundamentally, much greater interaction between paleo-scientists and policy makers is required to promote better science translation, data availability as well as to promote scientific literacy in government and industry, and policy literacy in the paleo-science community. Processes such as co-design are one way to achieve these aims, but require adequate resourcing, time and the collective will to collaborate. • Paleo-data provides a range of insights into long-term environmental change. • We identify barriers & opportunities to better assimilate paleo-insights into policy. • Communication between policy practitioners and paleo-scientists is a key challenge.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".