Refutation Texts in the Last Decade: A Bibliometric Exploration of Trends and Insights
Bibliographic record
Abstract
This study aims to explore research and publication trends related to refutation texts over the past ten years using bibliometric analysis. Data was obtained from the Scopus database of 115 documents published in 2015-2024 for “refutation texts” or “refutational texts” or “rebuttal texts”. The data obtained showed fluctuations in the number of publications, with the highest peak occurring in 2022. The main authors in this field include Kendeou, Sinatra, and Danielson, with the largest contributions coming from the United States, followed by European countries such as Germany and Canada. The dominant research subjects are social sciences and psychology, with a focus on the use of refutation texts to create cognitive conflict and change students’ conceptions. Keyword co-occurrence analysis shows a close relationship between refutation texts, misconceptions, and conceptual change. These findings highlight the importance of a refutation text-based approach in overcoming students’ misconceptions, as well as opening up opportunities for further research on its application in technology-based learning and student motivation.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.128 | 0.190 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".