Translations of scientific practice to “students' images of science”
Bibliographic record
Abstract
Abstract In the science education research literature, it often appears to be assumed that students “possess” more or less stable “images of science” that directly correspond to their experiences with scientific practice in science curricula. From cultural‐historical and sociocultural perspectives, this assumption is problematic because scientific practices are collective human activities and are therefore neither identical with students' experiences nor with the accounts of these experiences that students make available to researchers. “Students' images of science” are therefore translated from (rather than directly correspond to) scientific practices. Drawing on data collected during and after preuniversity biology students' internships in a scientific laboratory, we exemplify the role of these translations in the production of “students' images of science.” A coarse‐grained analysis based on existing research showed our data to be comparable with earlier studies on “students' images of science.” A fine‐grained analysis shows how “students' images of science” were coproduced along a trajectory of translations that was determined by the use of particular actions and tools, and a particular division of labor in scientific practice. We propose to reconceptualize “students' images of science” as particular coproductions at a given point in time. The methodological and educational implications of this proposal are discussed. © 2008 Wiley Periodicals, Inc. Sci Ed 93:611–634, 2009
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".