Databases, Scaling Practices, and the Globalization of Biodiversity
Bibliographic record
Abstract
Since the Convention on Biological Diversity in 1992, biodiversity has become an important topic for scientific research.Much of this research is focused on measuring and mapping the current state of biodiversity, in terms of which species are present at which places and in which abundance, and making extrapolations and future projections, that is, determining the trends.Biodiversity databases are crucial components of these activities because they store information about biodiversity and make it digitally available.Useful biodiversity databases require data that are reliable, standardized, and fit for up-scaling.This paper uses material from the EBONE-project (European Biodiversity Observation Network) to illustrate how biodiversity databases are constructed, how data are negotiated and scaled, and how biodiversity is globalized.The findings show a continuous interplay between scientific ideals related to objectivity and pragmatic considerations related to feasibility and data availability.Statistics was a crucial feature of the discussions.It also proved to be the main device in up-scaling the data.The material presented shows that biodiversity is approached in an abstract, quantitative, and technical way, disconnected from the species and habitats that make up biodiversity and the people involved in collecting the data.Globalizing biodiversity involves decontextualization and standardization.This paper argues that while this is important if the results of projects like EBONE are to be usable in different contexts, there is a risk involved as it may lead to the alienation from the organizations and volunteers who collect the data upon which these projects rely.
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 | 0.073 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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".