Bibliographic record
Abstract
Summary Van der Wal et al. (2015) (henceforth VdW) attempted to evaluate the degree to which the geographical origin of a species shapes people's attitudes towards conservation management decisions. Based on questionnaire surveys of the general public and experts from Scotland and Canada, the authors perceive “widespread use of the label ‘non-native’ as a proxy for harmfulness” and a species' origin as being used as shorthand for “harmfulness” (pp. 349 & 352). However, the authors cited by VdW do not take such a categorical view. Invasions of non-native species are also often just symptoms, not causes of human-created environmental changes. VdW focus on well-known species with long introduction histories for which the potential abundance and impact can plausibly be judged by experts and the public alike. However, when a decision is to be made regarding whether a non-native taxon that is not yet present in the local biota should be introduced, or whether a recently established and geographically restricted but spreading non-native taxon should be controlled, the taxon's origin should be a primary component in the decision-making process.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.188 | 0.071 |
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".