Re‐presenting the social construction of science in light of the propositions of Bruno Latour: For a renewal of the school conception of science in secondary schools
Bibliographic record
Abstract
Abstract Current opinion holds that school science has not been producing the expected outcomes. Highlighted by a considerable body of research, one of the concerns is that young people still mobilize a naive conception of science. Consequently, we must pursue the reflection process concerning ways of renewing the school conception of science so as to propose ways of representing and doing that are a greater source of participation among students. In this article, we argue for the importance of enriching school discourses by mobilizing a socialized conception of science. Our perspective seeks to highlight the major theoretical contributions of ethnomethodology put forward by Bruno Latour. Hence, we propose an operational definition of the social construction of science based on actor‐network theory as an attempt to produce a more focused conceptualization of science as a social enterprise. This theoretical work enables us to bring out various facets of the social construction of science that, in our view, should be accounted for in classes to renew the school image of science in line with the characteristics of contemporary technosciencesin the making. Finally, we illustrate how we can mobilize this understanding of science for secondary students. © 2009 Wiley Periodicals, Inc.Sci Ed94:743–759, 2010
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".