Preparing for change; challenges for fisheries governance
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Conveners: Alida Bundy (Canada), Marion Glaser (Germany), Annette Breckwoldt (Germany), Ingrid van Putten (Australia).CM 2018/H:196. Adaptation to climate change in ecosystem-based fisheries management of developed nations: how is social resilience governed? Woods, P.J., Macdonald, J., Barðarson, H., Baily, M., Bonanomi, S., Boonstra, W.J., Cornell, G., Cripps, G., Danielsson, R., Färber, L., Ferreira, A.S.A., Holma, M., Holt, R.E., Kokkalis, A., Langbehn, T., Ljungström, G., Nieminen, E., Nordström, M., Oostdijk, M., Richter, A., Romagnoni, G., Sguotti, C., Simons, A., Shackell, N., Snickars, M., Tunca, S., Whittington, J.D., Wootton, H., and Yletyinen, JCM 2018/H:72. A legal pluralism perspective on coastal fisheries governance in two Pacific Island countries. Janne R. Rohe, Hugh Govan, Achim Schlüter, Sebastian C.A. FerseCM 2018/H:261. Perfect is the enemy of good – when more accurate stock assessments are less valuable for management. Esther Schuch, Silke Gabbert, Andries RichterCM 2018/H:125. Ecological, socioeconomic and institutional resilience to shifting fish stocks. E. Fontán and E. Ojea.CM 2018/H:274. Best practice guidelines on developing decision support tools for European fisheries and aquaculture sectors. Thuy Thi Thanh Pham, Frank Wätzold, Astrid Sturm, Alan Baudron, Michaela AschanCM 2018/H:192. Stakeholders’ perceptions of Governance in Iceland and Spain (Galicia). Ixai Salvo, Rosa Chapela, Antonio G. AllutCM 2018/H:269. Integrated Ecosystem Assessments for marine management from concepts to practise in ICES. Christine Röckmann; Jennifer Bailey, Dorothy Dankel, Geret DePiper, Johanna Ferretti, Ana Rita Fraga, Sarah Gaichas, Susan Gardner, David Goldsborough, Leyre Goti, Rolf Groeneveld, Katell Hamon, Andrew Kenny, Marloes Kraan, Sebastian Linke, Sean Lucey, Angela Münch, Gerjan Piet, Patricia Pinto da Silva, Marina Santurtun, Jörn Schmidt; Patricia M. ClayCM 2018/H:553. Accounting for shifting distributions and changing productivity in the development and use of scientific advice for fisheries management. Melissa A. Karp, Jay Peterson, Patrick Lynch, Roger GriffisCM 2018/H:530. Towards a more effective common fisheries policy: applying an analytical framework of 17 criteria to identify priorities for reform. Johanna Ferretti, Tobias Belschner, Ralf Döring, Alexander Kempf, Sarah Kraak, Gerd Kraus, Harry V. Strehlow, Christopher ZimmermannCM 2018/H:190. Measuring good fisheries governance: What do stakeholders think? Ixai Salvo, Svein Jentoft, Rosa Chapela, Antonio G. Allut
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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".