Reimagining nature‐based coastal adaptation: A nested framework
Bibliographic record
Abstract
Abstract Nature‐based coastal adaptation is a subset of nature‐based solutions that has to this point focused on the materiality of managing coastal risks: what our coastal protections are made of or where we put things that are in the way of harm. In our collaborative interdisciplinary work, we have been reimagining nature‐based coastal adaptation to start with first principles: how we think about the coast and what makes a good coastal life. In a nature‐based approach our shared sense of what is good and possible, also known as the social imaginary, needs shifting before any physical material. This paper presents a new nested framework for thinking about nature‐based coastal adaptation using five words starting with R: Reimagine, Reserve, Relocate, Restore, Reinforce. We use the nature‐based adaptation option of managed dyke realignment in Bay of Fundy agricultural dykelands to illustrate the utility of the framework in practice but assert its more generic applicability.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".