Navigating the divide between scientific practice and science studies to support undergraduate teaching of epistemic knowledge
Bibliographic record
Abstract
There is an urgent need to strengthen undergraduate science students’ epistemic knowledge, which requires having the scientists qua teachers on board. The divide between scientists’ perceptions of science and the perceptions held by those who study science is in this context problematic. Even so, this remains a sorely understudied area. The aim of the study was to identify pragmatic ways that hold the potential to facilitate integration of scholarly studies of scientific knowledge production with experientially based knowledge held by scientists to support the teaching of epistemic knowledge content to undergraduate science students. Earlier studies suggest that trust building is a central component. Our exploratory case study focuses on instructor perceptions and is based on informal interviews, participatory observation and surveys with instructors in a first-year undergraduate science course under revision. We identified the following central components as central to successful navigation of the divide between the scientific practice and science studies: Explicit formulation of learning objectives tied to epistemic knowledge acquisition; Conscious attention to vocabulary that triggers scientists’ aversion to science studies; Careful selection of historic and contemporary cases; and Systematic scaffolding of course activities. The conclusion regarding a common vocabulary stands out: by ridding our instructions from the vocabulary that caused concern among science instructors we succeeded in engaging them in conversations with students about the knowledge-producing process and challenge the view of science as characterised by facts and truths, rather than a form of scholarly inquiry that aims to produce knowledge about the natural world.
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 | 0.047 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.004 | 0.008 |
| 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".