Bibliographic record
Abstract
Abstract: When applied to the sea, the wilderness concept of preserving large areas from human effects throws light on marine conservation ethics, pragmatism, and practice. I compared problems of land wilderness with those involving the sea, where social and technical preparedness for conservation lags behind that for the land. I describe marine applications related to wilderness and provide examples from various jurisdictions. Daunting marine attributes of scale, dynamism, and connectedness, as well as scientific and cultural uncertainty, weaken maritime application of the wilderness notion. Three main problems with this application are (1) protecting large enough areas, (2) defining desired marine ecosystem states with attendant roles for humans, and (3) addressing society's underdeveloped marine environmental awareness and ethics. The first relates to the practicality of establishing large preserved areas near populated compared with unpopulated areas. The second relates to accommodating coastal‐community subsistence given the ethics and realities of coastal poverty, indigenous peoples' status, and rural lifestyles. The third relates to public confusion over maritime governance, uncertainty over appropriate civility toward marine resources, and cultural expectations of freedom on the seas. These issues foster political indecision and strong opposition from some fishery and coastal‐community sectors to conservation of marine areas. Perhaps the value‐laden wilderness term still carries too much terrestrial preservationist baggage for sea areas that are viewed as commons by many. The wilderness ideal is but one point on a conservation continuum ranging from strict preservation to humans as part of nature. The laudable ideal of wilderness has a role in addressing the need for rapid progress in marine conservation, but we should be circumspect in applying the wilderness concept to the sea.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".