Bibliographic record
Abstract
Scientific evidence is easily misunderstood. One of the most insidious instances of misunderstanding arises when scientific experts and those receiving their evidence assign different meanings to the same words. We expect scientific evidence to be difficult to understand. What is unexpected, and often far more difficult to detect, is the incorrect understanding of terms and phrases that appear familiar. In these circumstances, misunderstandings easily escape notice. We applied an evidence-based approach to investigating this phenomenon, asking two groups, one with legal education and one with scientific education, to define five commonly-used phrases with both lay and scientific connotations. We hypothesized that the groups would significantly diverge in the definitions they provided. Employing a machine learning algorithm and the ratings of trained coders, we found that lawyers and scientists indeed disagreed over the meanings of certain terms. Notably, we trained a machine learning algorithm to reliably classify the authorship of the definitions as scientific or legal, demonstrating that these groups rely on predictably different lexicons. Our findings have implications for recommending avoidance of some of these particular words and phrases in favour of terminology that promotes common understanding. And methodologically, we suggest a new way for governmental and quasi-governmental bodies to study and thereby prevent misunderstandings between the legal and scientific communities.
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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 | 0.011 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".