Stop the Spam! Conference Ethics and Decoding the Subtext in Post-Truth Science. What Would Denis Diderot Say?
Bibliographic record
Abstract
Abstract Science and its practice always had a subtext, subject to influence by scientists', funders', and other innovation actors' values and assumptions. The recent emergence of post-truth, authoritarian and populist penchants, in both developed and developing countries, has further blurred the already fluid boundaries between material scientific facts and their social construction/shaping by scientific subtext, human values, powers, and hegemony. While there are certain checks, balances, and oversight mechanisms for publication ethics, other pillars of science communication, most notably, scientific conferences and their governance, are ill prepared for post-truth science. Worrisomely, the proliferation of spam conferences is a major cause for concern for integrative biology and postgenomic science. The current gaps in conference ethics are important beyond science communication because conferences help build legitimacy of emerging technologies and frontiers of science and, thus, bestows upon the organizers, funders, enlisted scientific advisors, speakers, among others, power, which in turn needs to be checked. Denis Diderot (1713–1784), a prominent intellectual during the Enlightenment period, has aptly observed that the very act of organizing brings about power, influence, and control. If the subtext of conference practices is left unchecked, it can pave the way for hegemony, and yet more volatile and violent authoritarian governance systems in science and society. This begs for innovative solutions to increase accountability, resilience, and capacity of technology experts and scientists to discern and decode the subtext in science and its communication in the current post-truth world. We propose that the existing undergraduate and graduate programs in life and physical sciences and medicine could be redesigned to include a rotation for exposure to and training in political science. Such innovative PhD+ programs straddling technical and political science scholarship would best equip future students and citizens to grasp and respond to subtext and embedded opaque value and power systems in scientific practices in an increasingly post-truth world. Political science scholarship unpacks the inner workings, subtext, and power dynamics in science and society. Thus, knowledge of political science competency is akin to molecular biology in life sciences. Both make the invisible (e.g., cell biology versus subtext of knowledge) visible. The ability to read subtext in science and claims of post-truth knowledge is a new and essential form of societal literacy in 21 st century science and integrative biology.
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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 | 0.056 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".