Integrating humanities and social sciences into marine ecosystem management - first steps
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Conveners: Patricia M. Clay (USA), Jörn Schmidt (Germany), David Goldsborough (the Netherlands).CM 2016/R:402. Science for an ecosystem approach: dilemmas in advisory processes. Sebastian Linke, Kåre Nolde Nielsen, Petter HolmCM 2016/R:442. Uncovering social and ecological changes in recreational fisheries using popular media. Ruth H Thurstan, John M PandolfiCM 2016/R:314. The Amsterdam Effect - multiple stakeholder cooperation for successful management of sharks and rays. Paddy Walker, Irene Kingma, Dale RodmellCM 2016/R:218. Food consumption as driver of nutrient inputs into marine environments. Anders Grimvall, Are Vallin, Eva-Lotta SundbladCM 2016/R:322. Justification theory to explore socio-cultural values related to fish and fisheries,Baltic salmon and herring cases. Suvi Ignatius, Päivi HaapasaariCM 2016/R:650. The Celtic Seas Partnership. Penny WilsonCM 2016/R:596. Coastal municipal governance and marine ecosystem management integration, stakeholders perception and action development. Erika Lagzdina, Raimonds Ernsteins, Ivars Kudrenickis, Janis Ulme, Janis KaulinsCM 2016/R:603. Tracking underutilized species in Rhode Island, U.S. – Understanding fish as food, not just commodity. Dawn M. Kotowicz, Azure Cygler, Elin Torell, Kristine BeranCM 2016/R:581. How to integrate fishermen knowledge and participation to improve the management advice of a deep-sea artisanal fishery . Inês Farias, Teresa Moura, Nuno Veiga, Neide Lagarto, Ivone FigueiredoCM 2016/R:468. Understanding financial risk for fishers and the role of diversification. Sanmitra GokhaleCM 2016/R:468. What drives changes in stock assessments?. Esther SchuchCM 2016/R:553. Towards the development of a process based model of the human dimension; a systems approach to fisheries management. M. Drexler, R.M. Bailey, E. Carella, M.G. Burgess, S. Saul, C. Dorsett, M. Clemence‐McCann, S. Wilcox, R. Cabral, R.L. Axtell, B. Owashi, C. Costello, S.D. Gaines, A. MerklCM 2016/R:599. Social and natural science integration in the Bering Sea Project; an economist's perspective. Alan C. HaynieCM 2016/R:528. Futures for the use of Baltic herring catch in 2040. M. Pihlajamäki, S. Sarkki, T.P. KarjalainenCM 2016/R:520. Using modeling approach and fishermen's knowledge to define suitable habitat for cusk (Brosme brosme) and evaluate potential economic impacts of conservation. Jocelyn Runnebaum, Yong ChenCM 2016/R:143. Building an Assessment Framework for the Goose Barnacle Fishery off the West Coast of Vancouver Island - Melding Analytical Methods with Indigenous Local Knowledge. Alex Gagne, Candace Picco CM 2016/R:326. Measuring good fisheries governance, Indicators, tools and stakeholders. Ixai Salvo, Svein Jentoft, Petter Holm, Jahn Petter Johnsen, Antonio G. AllutCM 2016/R:425. Balancing Ecology, Economy and Society in two Pioneering Marine National Parks in Scandinavia – Assessing institutional designs and perceived sustainability outcomes five years after establishment. Andrea Morf, Annica Sandström, Sverker C. JagersCM 2016/R:409. Strong connections, loose coupling: understanding environmental and social influences on commercial fisheries and subsistence harvests in the Bering Sea ecosystem, Alaska. Henry P. Huntington, Alan HaynieCM 2016/R:256. Global protein demand, marine fish production, and trade-flows in the world of 2050. Julia Hoffmann, Katrin Kamin, Linda Kleemann, Christian Möllmann, Martin F. Quaas, Jörn O. Schmidt, Guilherme A. Stecher, Justiniano Pinto, Rudi VossCM 2016/R:643. Evolution of integration of Human Dimensions into WGNARS - challenges and possibilities. Patricia M. Clay , Geret DePiper, Sarah Gaichas, Patricia Pinto da SilvaCM 2016/R:89. The Carrot, not the Stick; co-design, testing and buy-in of stakeholders for Real-time Incentive fisheries management. Debbi Pedreschi, Hannes Hoffle, Sarah Kraak, Amos Barkai, Keith Farnsworth, David Reid
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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 | 0.047 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.055 | 0.023 |
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".