New rationalities, inner logic, and hope for sustainable future coasts
Bibliographic record
Abstract
Abstract Non-technical Summary Human actions are causing climate change, pollution, and the loss of biodiversity, making our planet less safe. To address these problems, solutions must be developed from current and future research, involving different scientific fields and respecting diverse knowledge systems. It is essential to engage with society, as the relationship between science and society drives progress. Studying coasts as complex systems requires input from the natural sciences, social sciences, and humanities. Technical Summary In the coastal zone, the triple planetary crisis manifests as accelerating losses and changes and increasing challenges and risks for people and livelihoods. Acceptance of a future existential crisis compels the urgency of corrective action to cause an inverse positive societal response to bend the negative trajectories of loss and damage. The rate and extent of corrective societal action (policies, laws, practices, knowledge, etc.) should at least keep pace with the projected rate of loss and environmental degradation. This urgency and acceleration of action are major societal challenges, especially considering the overwhelming evidence of impacts. In this paper, we offer three propositions for accelerating urgent actions and fostering innovation in coastal research and management, focusing on emerging trends and foundational changes. Scientists need to (1) reflect on the performativity of their research and perceptions of neutrality in anticipating the future of coasts; (2) think and act equitably in local and global partnerships; and (3) improve their engagement and willingness to innovate with society. This is not a call for linear or incremental change, but a call for the radical. The relationship between society and science drives progress and shapes our collective future. Social media summary Human actions drive climate change, pollution, and biodiversity loss, threatening our planet. To address these crises, we need solutions that blend current and future research, span multiple scientific fields, and respect diverse knowledge systems. Engaging with society is key. The bond between human society and science shapes our future. Coastal studies must integrate natural sciences, social sciences, and humanities.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".