Fostering diversity, equity, and inclusion in interdisciplinary marine science
Bibliographic record
Abstract
Laura Kaikkonen1,2, Rebecca J Shellock3,4, Samiya Ahmed Selim5, Renis Auma Ojwala6, Beatriz S. Dias7, Shenghui Li8, Charles I. Addey9, Ignacio Gianelli10,11, Katherine M Maltby12, Sara Garcia-Morales13,14, Juliano Palacios-Abrantes15, Shan Jiang16, Marta Albo-Puigserver17, Virginia A. García Alonso18, Chelsey A. Baker19, Colleen B. Bove20, Stephanie Brodie21, Lol Iana Dahlet22,23, Jewel Das22,24, Aislinn Dunne25, Sebastian C.A. Ferse22,26, Ellen Johannesen6, Julia Jung27, Eugenia Merayo Garcia28, Denis B. Karcher29, Sarah Mahadeo6, Lucia Millan30, Kasali Oladepo Lawal31, Ayodele Oloko32, Kelly Ortega-Cisneros33, Stephanie Otoabasi-Akpan34, Durlave Roy35, Samina Sharmin Rouf36, Szymon Smoliński37, Natasa Vaidianu38,39, Chris Whidden40, Mia Strand41National Institute of Water and Atmospheric Research, New ZealandUniversity of Helsinki, FinlandInstitute for Marine and Antarctic Studies, Hobart, Tasmania, AustraliaCentre for Marine Socioecology, University of Tasmania, AustraliaCenter for Sustainable Development, University of Liberal Arts BangladeshWorld Maritime University-Sasakawa Global Ocean Institute, Malmö, SwedenCollege of Fisheries and Ocean Sciences, University of Alaska Fairbanks, USGuangdong Ocean University, ChinaDepartment of Oceanography, University of Hawaii at Manoa, USEqualSea Lab-CRETUS. Department of Applied Economics, Universidade de Santiago de Compostela, SpainSouth American Institute for Resilience and Sustainability Studies (SARAS), UruguayGulf of Maine Research Institute, Maine, USMarine Environmental and Sciences Centre (MARE-ARNET), University of Lisbon, PortugalEcology and Biodiversity Institute (IEB), ChileInstitute for the Oceans and Fisheries, The University of British Columbia, Vancouver CanadaState Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, ChinaCentro Oceanográfico de Baleares, Instituto Español de Oceanografía (IEO‑CSIC), Ecosystem Oceanography Group, Palma, SpainInstituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA, CONICET-UBA)National Oceanography Centre, Marine Systems Modelling, Southampton, UKUrsinus College; Biology Department; Collegeville, United StatesEnvironment, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, AustraliaLeibniz Centre for Tropical Marine Research (ZMT), GermanyInstituto de Estudos Costeiros, Universidade Federal do Pará (UFPA), Bragança, BrazilInstitute of Marine Sciences, University of Chittagong, Chattogram, BangladeshRed Sea Research Center, King Abdullah University of Science and Technology, Saudi ArabiaDepartment of Marine Ecology, Faculty of Biology and Chemistry, University of Bremen, Bremen, GermanyCobra Collective, Egham, United KingdomJoint Nature Conservation Committee, UKAustralian National Centre for the Public Awareness of Science, Australian National University, Canberra, AustraliaInstitut de Ciències del Mar - CSIC, Barcelona, SpainDepartment of Marine Science and Technology, Federal University of Technology Akure, NigeriaInstitute for the Oceans and Fisheries, The University of British Columbia,CanadaDepartment of Biological Sciences, University of Cape Town, Cape Town, South AfricaFederal University of Technology, AkureBangladesh Open UniversityBertarelli Foundation’s Marine Science Programme, Zoological Society of London, UKNational Marine Fisheries Research Institute, Gdynia, PolandFaculty of Natural and Agricultural Sciences, Ovidius University of ConstantaInterdisciplinary Center of Advanced Research on Territorial Dynamics, University of Bucharest, RomaniaFaculty of Computer Science, Dalhousie UniversityDepartment of Development Studies, Nelson Mandela University, Gqeberha, South Africa
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.056 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".