Proactive, Reactive, and Inactive Pathways for Scientists in a Changing World
Bibliographic record
Abstract
Abstract As atmospheric CO2levels continue to rise so too does the risk of severe impacts. Scientists clearly have an important role to play in preparing for and responding to climate change impacts; however, calls by scientists for global action have not led to the required changes. It is timely, therefore, for scientists to critically consider their own approach toward climate change research, particularly if we are to ameliorate or adapt to unwanted outcomes. Here we present three different pathways that allow scientists and scientific institutions to conceptualize the implications of their responses to climate change scenarios. These pathways are illustrated via three plausible futures for the marine environment under climate change. This approach allows future responsibilities, outcomes, and implication to be explored within and across pathways and can be applied to different scenarios for scientists and scientific institutions to anticipate and better prepare to contribute effectively to the future.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".