Organized Science Denial: Reviving a Symposium Discussion to Propose Actionable Plans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it