A Compilation, Analysis, and Categorization of 403 Atmospheric and Climate Science Misconceptions
Bibliographic record
Abstract
The National Association of Geoscience Teachers (NAGT) has listed the inaccessibility of research related to misconceptions in atmospheric and climate science as a Geoscience Education Research Grand Challenge (Cervato et al. 2018). This project was a direct response to this call for research and consisted of three distinct steps: 1) data gathering, which included reviewing the literature for relevant misconception data, 2) a qualitative analysis, which included compiling, organizing, and categorizing the data collected, and 3) a quantitative analysis, which included determining the prevalence of each misconception across topic categories, demographic categories, and over time. A total of 403 misconceptions related to atmospheric and climate science have been identified. Of those misconceptions, nearly half (approximately 47%) were related to climate change. A little less than a third of the misconceptions (approximately 32%) were related to fundamental atmospheric science topics. Finally, less than a quarter of the misconceptions (22%) were related to general climate concepts (excluding climate change). Of the 403 misconceptions, 80% were identified in students, 16% were identified in teachers and 4% were identified in both students and teachers. Most of the identified misconceptions have not been well-studied. In fact, 74% of the identified misconceptions were only identified by a single study, and only 1.5% were identified by five or more studies. This compilation of misconceptions can serve as a guide for future misconceptions research and as a resource for educators. Advisor: Dawn Kopacz
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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.023 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".