Keeping Humans in the Ecosystem
Bibliographic record
Abstract
Abstract The World Ocean presents many opportunities, with the blue economy projected to at least double in the next two decades. However, capitalizing on these opportunities presents significant challenges and a multi-sectoral, integrated approach to managing marine socio-ecological systems will be required to achieve the full benefits projected for the blue economy. Integrated ecosystem assessments have been identified as the best means of delivering the information upon which marine resource management decisions can be made. By their nature, these assessments are inter-disciplinary, but to date have mostly focused on the natural sciences. Inclusion of human dimensions into integrated ecosystem assessments has been lagging, but is fundamental. Here we report on a Symposium, and the articles emmanating from it that are included in this Theme Set, that address how to more effectively include human dimensions into integrated ecosystem assessments. We provide an introduction to each of the main symposium topics (governance, scenarios, indicators, participatory processes, and case studies), highlight the works that emerged from the symposium, and identify key areas in which more work is required. There is still a long way to go before we see end-to-end integrated ecosystem assessments inclusive of all the major current and potential ocean use sectors that also encompass multiple aspects of human dimensions. Nonetheless, it is also clear that progress is being made and we are developing tools and approaches, including the human dimension, that can inform management and position us to take advantage of the multi-sectoral opportunities of sustainable blue growth.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.007 |
| 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".