The Intersections of Biological Diversity and Cultural Diversity: Towards Integration
Bibliographic record
Abstract
"There is an emerging recognition that the diversity of life comprises both biological and cultural diversity. In the past, however, it has been common to make divisions between nature and culture, arising partly out of a desire to control nature. The range of interconnections between biological and cultural diversity are reflected in the growing variety of environmental sub-disciplines that have emerged. In this article, we present ideas from a number of these sub-disciplines. We investigate four bridges linking both types of diversity (beliefs and worldviews, livelihoods and practices, knowledge bases and languages, and norms and institutions), seek to determine the common drivers of loss that exist, and suggest a novel and integrative path forwards. We recommend that future policy responses should target both biological and cultural diversity in a combined approach to conservation. The degree to which biological diversity is linked to cultural diversity is only beginning to be understood. But it is precisely as our knowledge is advancing that these complex systems are under threat. While conserving nature alongside human cultures presents unique challenges, we suggest that any hope for saving biological diversity is predicated on a concomitant effort to appreciate and protect cultural diversity."
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".