Science maps for biogeography—The field's place within the sciences and its change over the past quarter century
Bibliographic record
Abstract
Abstract Aim The field of biogeography is often described as a hub between research disciplines. Here we apply science mapping to study how biogeography has changed and evolved between 1995 and April 2022, and to analyse the mix of disciplines used in this field. We explore how research foci have changed over time and if biogeographical topics have entered the public discourse. Location Local to global. Taxon All taxa. Methods We created a semantic map of the field based on co‐occurrences of keywords or composite keywords from 40,000+ scientific papers published between the 1970s and April 2022, structuring these data into three hierarchical sets. A fourth set of Top 100 researchers was constructed in September 2022. To address our public‐discourse question, we used the Factiva archive of global media. Results Five core journals contained 14,386 papers (or 35.7% of the full set), while the remaining papers appeared in 2247 journals of which 59% included only one or two biogeographical papers. Since 1995, frequencies of keywords related to core concerns of biogeography have remained stable or even decreased, while ‘computing’ and ‘climate change’ have increased. There is an increasing association with Mathematics & Statistics, Computer Sciences, and Planning & Management, and a decreased association with Physical Geography. Biogeography‐related terms increasingly appearing in the public discourse are ‘biodiversity’, ‘urban nature’, ‘conservation’, ‘extinction’ and ‘rewilding’, while more technical concepts, such as ‘ecoregions’, ‘macroecology’ and ‘island biogeography’ remain at very low rates. Main Conclusions Biogeographical research is moving towards the social sciences, probably linked to a growing concern over global environmental issues and the Anthropocene. It is difficult to disentangle to what extent the public discourse is influenced by biogeographical research or vice versa, but ‘rewilding’ and ‘extinction’ are examples of topics that began in basic ecological‐biogeographical research and are now debated publicly.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.037 | 0.052 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".