Scientific Certainty Argumentation Methods (SCAMs): Science and the Politics of Doubt*
Bibliographic record
Abstract
At least since the time of Popper, scientists have understood that science provides falsification, but not “proof.” In the world of environmental and technological controversies, however, many observers continue to call precisely for “proof,” often under the guise of “scientific certainty.” Closer examination of real‐world disputes suggests that such calls may reflect not just a fundamental misunderstanding of the nature of science, but a clever and surprisingly effective political‐economic tactic—“Scientific Certainty” Argumentation Methods, or SCAMs. Given that most scientific findings are inherently probabilistic and ambiguous, if agencies can be prevented from imposing any regulations until they are unambiguously “justified,” most regulations can be defeated or postponed, often for decades, allowing profitable but potentially risky activities to continue unabated. An exploratory examination of previously documented controversies suggests that SCAMs are more widespread than has been recognized in the past, and that they deserve greater attention in the future.
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.
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 | 0.083 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".