From metrics to meaning: diversity as an essentially contested concept
Bibliographic record
Abstract
Biodiversity is among ecology’s most widely invoked but least consistently defined concepts. Despite decades of theoretical and methodological developments, ecologists continue to disagree about what "diversity" measures and how it should be quantified. These disagreements have direct consequences to conservation science, policy making, and public engagement. We argue that this enduring debate is not a sign of conceptual failure but evidence that "diversity" is an "essentially contested concept" (ECC) sensu Gallie (1955). Diversity is internally complex, appraisive, and historically dynamic: it comprises multiple, value-laden dimensions whose relative importance depends on context and purpose. Using examples from taxonomic diversity, we show that common metrics (e.g., richness, Shannon, Simpson, and Hill numbers) encode distinct value judgments (normative commitments) about how abundances shape diversity and, in turn, determine which species are considered most important to biodiversity. Likewise, spatial frameworks such as additive and multiplicative partitioning embody different normative assumptions about whether diversity represents the sum of local contributions or the relationships among communities. Beyond metrics, both definitional ambiguity and persistent knowledge shortfalls reinforce diversity’s plural and evolving character. Our goal is to show that recognizing diversity as an ECC is not a weakness of ecological thought, but rather a call for more reflective, inclusive, responsible research, teaching, and policy, anchoring biodiversity science within a richer understanding of its ethical and epistemic foundations. Diversity’s very "elusiveness" is what makes it powerful: it invites ecologists, policymakers, and citizens alike to reflect on what they value in the living world and to be explicit about how those values shape science and decision-making. Recognizing biodiversity as an ECC reframes disagreement as an opportunity rather than an obstacle. It calls for transparency about normative commitments, pluralism in measurement, and reflexivity in linking ecological analysis to ethical and political goals. Treating diversity as an ECC clarifies why no single metric can be definitive and offers a principled foundation for navigating conceptual plurality across ecological research, conservation planning, and global biodiversity governance.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.098 |
| Scholarly communication | 0.019 | 0.040 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".