Bibliographic record
Abstract
Improving the clarity of writing in ecology is a worthy goal, both among scientists and in communicating with policymakers and the public. Herrando-Pérez and colleagues (2014) propose a convention on ecological nomenclature (CEN) that would attempt to regulate ecological terminology. Any such attempt at terminological prescription will fail, because of fundamental properties of language and meaning. Attempts at language prescription are common and have a uniform history of failure (Hodges 2008). These failures are inevitable, as philosophers of science and language have shown. Many scientific terms simply cannot be defined with the prescriptive boundaries Herrando-Pérez and colleagues propose. Even a term such as forest illustrates the point: Although we all “know” what a forest is, we immediately run into trouble bounding its meaning: How much land is required? What tree density demarcates woodland from forest? Does a large thicket of baby trees count? Any fixed definition necessarily excludes cases, forcing one of two solutions: Either the definition must expand to accommodate excluded cases, thus obviating the prescribed definition, or new terms are required for excluded cases, leading to terminological proliferation. Since ecologists regularly apply old concepts to new systems, this problem is perennial. The CEN proposal fails to address this key philosophical issue.
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 | 0.041 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".