Hedging in Medical Articles from Two Pandemics
Bibliographic record
Abstract
As academic and scientific disciplines continue to evolve, it remains essential for scholars to present their claims with caution. Hedging, a vital linguistic tool, is pervasive in academic writing, although its boundaries are not strictly defined. This research tracks the changes in the use of hedging within medical literature, underpinning the idea that linguistic patterns reflect societal changes. Our study focuses on the analysis of hedging devices within a corpus of 30 medical articles, spanning two distinct pandemic periods: the 1918-1919 influenza pandemic and the 2020-2021 COVID-19 pandemic. A comprehensive review, involving contextual analysis, was conducted for each article to identify hedging instances. Types of hedges were documented and their frequency was calculated, while ambiguous cases were clarified through in-depth discussions and consistency checks. Our analysis confirms that contextual conditions influence both the frequency and types of hedges used. The results show a significant decrease in overall hedging frequency between the two pandemic periods, with approximators declining sharply while shields remained stable. This shift, along with a reduction in the variety of hedging devices used, suggests an evolution towards more precise quantification and a more formulaic style in scientific writing, while maintaining caution in knowledge claims. The conclusions drawn in this paper contribute to our understanding of scientific discourse and its evolution.
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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 | 0.017 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".