Informal Science Educators’ Views about Nature of Scientific Knowledge
Bibliographic record
Abstract
Publications such as Surrounded by science: Learning science in informal environments [Fenichel, M., & Schweingruber, H. A. (2010). Washington, DC: The National Academies Press] and Learning science in informal environments: People, places, and pursuits [National Research Council. (2009 National Research Council (2009). Learning science in informal environments: People, places, and pursuits, Washington, DC: National Academy Press. [Google Scholar]). Washington, DC: National Academy Press] have documented a recent trend advocating a greater awareness and value of Nature of Science (NOS), also known as Nature of Scientific Knowledge [see Lederman, N. G. (2007). Nature of science: Past, present, and future. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 831–879). Mahwah, NJ: Lawrence Erlbaum], for informal science learning experiences. However, little in the literature addresses what informal science educators know about science and, in particular, the views they have about NOS. In this study, informal science educators were asked to fill out an online version of the Views of Nature of Scientific Knowledge—Form C questionnaire [Abd-El-Khalick, F., & Lederman, N. G. (2000). The influence of history of science courses on students’ views of nature of science. Journal of Research in Science Teaching, 37(10), 1057–1095; Lederman, N. G., Schwartz, R. S., Abd-El-Khalick, F., & Bell, R. L. (2001). Preservice teachers’ understanding and teaching of nature of science: An intervention study. The Canadian Journal of Science, Mathematics, and Technology Education, 1(2), 135–160]. Along with interview responses, 20 of the fully completed questionnaires were purposefully selected to provide the data for this study. Participants were included if they were actively teaching full time to the public in informal science settings. This criterion for inclusion was utilized in order to address the lack of research about this category of individuals. The surveys underwent qualitative analysis and were coded using a scoring rubric. Overall, participants' demonstrated a strong understanding about NOS but views about the certainty of science were prevalent.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".