Bibliometric Analysis of the Structure and Evolution of Research on Assisted Migration
Bibliographic record
Abstract
Abstract Purpose of Review Assisted migration is increasingly proposed as a proactive management strategy to mitigate the consequences of maladaptation predicted under climate change. Exploring the social and academic structure of the field, its research gaps, and future research directions can help further the understanding and facilitate the implementation of assisted migration strategies. Here we used bibliometric analysis to examine the intellectual, social, and conceptual structures of assisted migration research to identify gaps and opportunities for future research. Bibliometric data based on publications on assisted migration were collected from Scopus and Web of Science databases using assisted migration and climate change or their synonyms as queries. Metadata were merged, processed and several networks were constructed. Recent Findings Co-citation and keyword co-occurrence networks identified three major clusters focused on (i) theory and risk of assisted migration of threatened and endangered species, (ii) impact of climate change on realized and fundamental climate and geographic niches, and (iii) assisted population migration. Collaboration network analysis identified three social core hubs: North America, Europe, and Australia, with the USA and Canada being the most productive and the most collaborative countries. Summary We conclude that future research is expected to concern mainly the assessment of physiological response of species and populations to extreme climate events such as drought and frost, and the contribution of non-climatic factors and biotic interactions in local adaptation and population performance under climate change. Social core hubs distinguished in this work can be used to identify potential international research and training collaborators necessary to address gaps and challenges underlying assisted migration implementation.
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 | 0.017 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.224 | 0.296 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".