Making Experts: The Boundary Crossing of Knowledge and the Emergence of Relational Expertise in a School Makerspace
Bibliographic record
Abstract
Existing research has illuminated the multidimensional nature of knowledge creation in school makerspaces. Yet, limited research exists on the boundary crossing of knowledge in makerspaces and how it can lead to the emergence of relational expertise. Using video records of interactions between 10–13-year-old students and their teachers in a school makerspace, this ethnographic case study applied mediated discourse analysis to investigate the boundary crossing of knowledge and the emergence of relational expertise—i.e., engaging with one’s own expertise, while recognizing, responding to, and building on others’ expertise. The results demonstrate how relational expertise emerged through boundary crossing of knowledge, with increased opportunities for students to identify themselves as experts. The boundary crossing of knowledge was mediated by participating students and teachers as well as material objects, evidencing the social and material nature of relational expertise in the makerspace. By recognizing the makerspace as a boundary object and an epistemic tool, the study enhances current understanding of the boundary crossing of knowledge and the emergence of relational expertise within creative and digitally enhanced learning environments.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".