Scholarly discourse in polarizing interdisciplinary contexts
Bibliographic record
Abstract
“Let us not speak of them; but look, and pass on.” (Dante, 1867/1320, Inferno, Canto III, line 51). These words could apply to both our original work (Ahmed et al. 2025a) and the response by South et al. (2025). In Ahmed et al. (2025a), we assembled a diverse, interdisciplinary team spanning natural and social sciences to critically unpack the risks of conflating biological invasions and human migration. This effort was met with a response that selectively quoted, misrepresented, and misconstrued our argument; showcasing that decontextualization and unsubstantiated inferences do not merely distort academic dialogue and impair the collaborative spirit but undermine the very foundations of scholarly exchange in contentious interdisciplinary terrain. This situation highlights a broader issue, prompting reflection and renewed attention to the principles of academic freedom, epistemic honesty, and the responsibility of critique, especially when sensitive human realities are at stake. There is an ever-growing need for rigorous scholarly inquiry to address the increasingly complex, interdisciplinary problems that emerge at the intersection of biological and social domains. Recent academic and public debates illustrate how approaches, tools, and terminologies can yield unintended sociopolitical consequences and conceptual confusion when uncritically applied between disciplines. Such missteps risk compromising the very conditions under which academic freedom, editorial independence, and intellectual pluralism can thrive, particularly when dealing with sensitive human realities (Green 2023, Speri 2025). In the spirit of Karl Popper's assertion that “the growth of knowledge depends entirely on disagreement” (Popper 1994), we believe it is crucial to engage these dynamics openly and reflectively (Russell and Blackburn 2017).
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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 | 0.101 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.035 | 0.179 |
| Scholarly communication | 0.039 | 0.044 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".