Representing scientific activity: Affordances and constraints of central design and enactment features of a model‐based inquiry unit
Bibliographic record
Abstract
Abstract This research explores how explaining an anchoring phenomena and engaging students in investigations, as central designs of a model‐based inquiry (MBI) unit, afforded or constrained the representation of scientific activity in the science classroom. This research is considered timely as recent standards documents and scholars in the field have highlighted the significance of identifying what features of scientific activity are important and how these can be represented for students in classrooms. Through taking advantage of qualitative research methods to closely examine the enactment of an MBI unit, both affordances and constraints were identified for each design. More specifically, explaining an anchoring phenomenon provided a context for more authentically framing the work of students, while investigations afforded students insight into the role these play in the refinement of models. Further, the teacher's attempts to support student reasoning and, at times, reasoning for students when they were found struggling were the most salient constraints identified connected to explaining an anchoring phenomenon and engaging students in investigations.
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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 | 0.030 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".