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A systematic global stocktake of evidence on human adaptation to climate change
Lea Berrang‐Ford, A.R. Siders, Alexandra Lesnikowski, A. Paige Fischer, Max Callaghan, Neal Haddaway, Katharine J. Mach, Malcolm Araos, Mohammad Aminur Rahman Shah, Mia Wannewitz, Deepal Doshi, Timo Leiter, Custódio Matavel, Justice Issah Musah-Surugu, Gabrielle Wong‐Parodi, Philip Antwi‐Agyei, Idowu Ajibade, Neha Chauhan, William Kakenmaster, Caitlin Grady, Vasiliki Ι. Chalastani, Kripa Jagannathan, Eranga K. Galappaththi, Asha Sitati, Giulia Scarpa, Edmond Totin, Katy Davis, Nikita Charles Hamilton, Christine Kirchhoff, Praveen Kumar, Brian Pentz, Nicholas P. Simpson, Emily Theokritoff, Delphine Deryng, Diana Reckien, Carol Zavaleta-Cortijo, Nícola Ulibarrí, Alcade C. Segnon, Vhalinavho Khavhagali, Yuanyuan Shang, Luckson Zvobgo, Zinta Zommers, Jiren Xu, Portia Adade Williams, Iván Villaverde Canosa, Nicole van Maanen, Bianca van Bavel, Maarten van Aalst, Lynée L. Turek‐Hankins, Hasti Trivedi, Christopher H. Trisos, Adelle Thomas, Shinny Thakur, Sienna Templeman, Lindsay C. Stringer, Garry Sotnik, Kathryn Dana Sjostrom, Chandni Singh, Mariella Siña, Roopam Shukla, Jordi Sardans, Eunice A. Salubi, Lolita Shaila Safaee Chalkasra, Raquel Ruiz‐Díaz, C. Richards, Pratik Pokharel, Jan Petzold, Josep Peñuelas, Julia Pelaez Avila, Julia B. Pazmino Murillo, Souha Ouni, Jennifer Niemann, Miriam Nielsen, Mark New, Patricia Nayna Schwerdtle, Gabriela Nagle Alverio, C. Mullin, Joshua Mullenite, Anuszka Mosurska, Michael D. Morecroft, Jan C. Minx, Gina Maskell, Abraham Marshall Nunbogu, Alexandre Magnan, Shuaib Lwasa, Megan Lukas-Sithole, Tabea Lissner, Oliver Lilford, Steven Koller, Matthew Jurjonas, Elphin Tom Joe, Lam Thi Mai Huynh, Avery P. Hill, Rebecca R. Hernandez, Greeshma Hegde, Tom Hawxwell, Sherilee L. Harper, Alexandra Harden, Marjolijn Haasnoot, Elisabeth Gilmore, Leah Gichuki, Alyssa Gatt, Matthias Garschagen, James D. Ford, Andrew Forbes, Aidan D. Farrell, Carolyn A. F. Enquist, Susan J. Elliott, Emily Duncan, Erin Coughlan de Perez, Shaugn Coggins, Tara Chen, Donovan Campbell, Katherine E. Browne, Kathryn Bowen, Robbert Biesbroek, Indra D. Bhatt, Rachel Bezner Kerr, Stephanie Barr, Emily Baker, Stéphanie Austin, Ingrid Arotoma‐Rojas, Christa M. Anderson, Warda Ajaz, Tanvi Agrawal, Thelma Zulfawu Abu
Bibliographic record
VenueNature Climate Change · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsWilfrid Laurier UniversityUniversity of OttawaUniversity of WaterlooUniversity of TorontoInternational Development Research CentreMcGill UniversityUniversity of GuelphThe Scarborough HospitalUniversity of AlbertaUniversité LavalConcordia University
FundersEconomic and Social Research CouncilSocial Sciences and Humanities Research Council of CanadaChina Scholarship CouncilMinistry of Education, IndiaUniversity Grants CommissionDirectorate for GeosciencesMinisterio de Ciencia e InnovaciónNational Institute for Health and Care ResearchInternational Development Research CentreAgence Nationale de la RecherchePortland State UniversityMinisterio de Ciencia, Innovación y UniversidadesDivision of Human Resource DevelopmentStudienstiftung des Deutschen VolkesGovernment of the United KingdomAgence Française de DéveloppementNational Academies of Sciences, Engineering, and MedicineGulf Research ProgramNational Science Foundation
KeywordsAdaptation (eye)Climate changeEcological forecastingClimate change adaptationEnvironmental resource managementSystematic reviewPolitical scienceGlobal warmingRisk analysis (engineering)EcologyBusinessEnvironmental sciencePsychologyBiologyMEDLINE
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.107
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.184
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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.
Opus teacher head0.387
GPT teacher head0.431
Teacher spread0.044 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview
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".
Quick stats
Citations613
Published2021
Admission routes2
Has abstractno