Lab technicians and high school student interns—Who is scaffolding whom?: On forms of emergent expertise
Bibliographic record
Abstract
Abstract Apprenticeship and the associated support mechanism of scaffolding have received considerable interest by educational researchers as ways of inducting students into science. Most studies treat scaffolding as a one‐way process, where the expert supports the development of the novice. However, if social processes generally and conversations specifically are dialogical in nature then we would expect to observe two‐way processes. The purpose of this paper is to report the results of an ethnographic study of high school students' internships in a scientific laboratory. Data were collected through observation, fieldnotes, and videotaping. Drawing on discursive psychology and conversation analysis, we find that laboratory technicians and students draw on different forms of discursive strategies to articulate knowledgeability while transacting with each other. We put forth the notion of emergent expertise to describe new forms of expertise that are not a property of individuals but rather the product of collective transactions. Our study illustrates the importance of opportunities generated in the internship for both old‐timers and newcomers to bring about knowledgeability. This study implies a rethinking of the role of the expert and the notion of scaffolding, which puts more emphasis on the transactional process rather than on learners as recipients. © 2008 Wiley Periodicals, Inc. Sci Ed 93:1–25, 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 | 0.022 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".