Beyond guides, protocols and acronyms: Adoption of good modelling practices depends on challenging academia's status quo in ecology
Bibliographic record
Abstract
Implementing good modelling practices (GMP) in ecological sciences is key to improving scientific reliability. Despite the increased availability of guidelines and protocols detailing how principles such as FAIR and PERFICT can be implemented to improve good modelling practices, the sharing of code which can reproduce results and workflows remains remarkably low. In this work, we explore potential root causes of this discrepancy. We identify three key factors inherent to the current academic structure that, in our experience, might play a role in hindering a wider adoption of GMP: (1) acknowledgment of the time required to implement GMP in projects, (2) the lack of GMP and software development training among ecologists, and (3) perception of GMP as unrewarding in the short-term. We argue that there is an urgent need for systemic changes. Such changes include (1) a cultural shift to value the incorporation of GMP across projects, emphasising the need for explicit budget allocation and careful scheduling of its implementation, (2) redesigning academic curricula to explicitly include GMP and software development as fundamental disciplines in ecology, and (3) an increase in recognition of open and functional code and workflows for career advancement. We call for concerted efforts for bridging this gap, and propose a hopeful outlook emphasising the role of a new generation of scientists and tools committed to good science. Proposing concrete actions, we aim to start a discussion on challenging academia's status quo in ecology and support scientists in bringing a significant paradigm shift to ecological modelling.
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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 | 0.111 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.025 |
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".