The value of an emergent notion of authenticity: Examples from two student/teacher–scientist partnership programs
Bibliographic record
Abstract
Abstract We make the case for an emergent notion of authenticity of science based on systems theory and neo‐Piagetian thought. We propose that authentic science is an emergent property of a dynamic system of learning precipitated by the interactions among students, teachers, and scientists that occur within the contexts defined by the internal and external constraints of the cultures of the schools and communities within which they operate. Authenticity as an emergent property of the learning process challenges the basis for many science curricula and current pedagogical practices that take scientists' science as their norm and that assume a priori that such is authentic, i.e., it practices preauthentication. We argue that what constitutes authentic science can be taught neither in the traditional didactic modes nor through simulations of scientists' science in the classroom. Instead, authenticity needs to be seen as emergent and as diverse in meaning. To illustrate this point, we draw from two different face‐to‐face, teacher/student–scientist partnership programs. Both studies support a notion of authenticity that emerges as teachers, students, and scientists come to interact, make meaning of, and come to own the activities they engage in collaboratively. We conclude by considering the implications of such an analysis for science education. © 2003 Wiley Periodicals, Inc. J Res Sci Teach 40: 737–756, 2003
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| 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".