Assessing Transparency and Reproducibility in Invasion Science
Bibliographic record
Abstract
Policymakers and practitioners overseeing invasive species management depend on reliable research for guidance. Transparency and reproducibility are core features of reliable research, and prerequisites for successful study replication, but are evidently lacking in many science disciplines. Whether this shortfall characterizes invasion science remains unknown. We evaluated a sample of invasion science studies for their adherence to practices that enhance transparency and reproducibility, such as making data and code available, and explicitly considering statistical power. Our evaluation focused on published studies concerning two plant species invasive to riparian ecosystems in British Columbia, Canada (Elaeagnus angustifolia and Phalaris arundinacea), as these contributed to a broader systematic mapping initiative. Our systematic literature search yielded 746 studies, of which 45 met our predefined inclusion criteria (relevance rate = 6%). We assessed each study against a 14-item checklist (motivated by the Transparency and Openness Promotions guidelines) and a corresponding scoring rubric. On average, studies achieved a score of 26%, with no studies addressing statistical power, pre-registering their plans, and few making data or code publicly available. There is a clear need and opportunity for improving the transparency and reproducibility of invasion science research. We refer researchers to resources aimed at improving research practices, and discuss two practices that are especially important in the context of applied invasion science: power analysis and sharing data and code. We echo recent calls for educational and research institutions to expand access to training in open science, and urge policymakers and practitioners to consider transparency and reproducibility when seeking guidance from invasion science research.
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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 | 0.714 | 0.883 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".