Logical fallacies persist in invasion biology and blaming the messengers will not improve accountability in this field: a response to Frank et al.
Bibliographic record
Abstract
We analyze the "Logical fallacies and reasonable debates in invasion biology: a response to Guiaşu and Tindale" article by Frank et al., and also discuss this work in the context of recent intense debates in invasion biology, and reactions by leading invasion biologists to critics of aspects of their field. While we acknowledge the attempt by Frank et al., at least in the second half of their paper, to take into account more diverse points of view about non-native species and their complex roles in ecosystems, we also find the accusations of misrepresenting invasion biology, for instance by "cherry-picking" and "constructing 'straw people'", directed at the Guiaşu and Tindale study to be unwarranted. Despite the sometimes harsh responses by leading invasion biologists to critics of their field, we believe that persistent and fundamental problems remain in invasion biology, and we discuss some of these problems in this article. Failing to recognize these problems, and simply dismissing or minimizing legitimate criticisms, will not advance the cause, or enhance the general appeal, of invasion biology and will prevent meaningful progress in understanding the multiple contributions non-native species can bring to various ecosystems worldwide. We recommend taking a more open-minded and pragmatic approach towards non-native species and the novel ecosystems they are an integral part of.
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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 | 0.066 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.043 | 0.061 |
| Insufficient payload (model declined to judge) | 0.002 | 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".