Organized Science Denial: Reviving a Symposium Discussion to Propose Actionable Plans
Bibliographic record
Abstract
This symposium builds on a successful session organized for the virtual Academy of Management meeting in Philadelphia (2021), titled “Science Denial: Causes, Courses, and Remedies. A Route Map for Organizational Scholars,” which inspired the publication of an edited volume on the same topic, recently accepted by Oxford University Press and titled “Organized Science Denial. An Action Plan of Solutions”. The overall objective of this Symposium is to reflect upon the common thread linking seemingly unrelated phenomena, rooted in the rejection of science, highlighting their profound implications for organizations and society. Specifically, from this Symposium participants will gain updated insights into the evolving nature of science denialism, its links to issues such as greenwashing and communicative strategies, the undeniable key role of social platforms in current days and in the future, and the tensions within the social sciences and management disciplines, among others. More broadly, the AoM community will learn not only actionable strategies for addressing science denialism, but also how organizational scholars contribute meaningfully can contribute to academic discourse. Keywords: communication and rhetoric, creativity, greenwashing, institutional theory, internal tension, science denialism, social media platforms.
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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 | 0.086 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.019 | 0.042 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".