Bibliographic record
Abstract
In the literature on the situated and distributed nature of cognition, the coordination of spatial organization and the structure of human practices and relations is accepted as a fact. To date, science educators have yet to build on such research. Drawing on an ethnographic study of high school students during an internship in a scientific research laboratory, which we understand as a “perspicuous setting” and a “smart setting,” in which otherwise invisible dimensions of human practices become evident, we analyze the relationship between spatial configurations of the setting and the nature and temporal organization of knowing and learning in science. Our analyses show that spatial aspects of the laboratory projectively organize how participants act and can serve as resources to help the novices to participate in difficult and unfamiliar tasks. First, existing spatial relations projectively organize the language involving interns and lab members. In particular, spatial relations projectively organize where and when pedagogical language should happen; and there are specific discursive mechanisms that produce cohesion in language across different places in the laboratory. Second, the spatial arrangements projectively organize the temporal dimensions of action. These findings allow science educators to think explicitly about organizing “smart contexts” that help learners participate in and learn complex scientific laboratory practices.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".