Makerspaces as complex sociomaterial assemblages
Bibliographic record
Abstract
The emergence of makerspaces is an outgrowth of our current educational and technological era. While making is not new, networking capabilities has made it relatively easy to locate materials, knowledge, procedures, and expertise. Through technologies that are now affordable to consumers, there is a folding of human activity, digital, and material; that is, these practices, previously viewed as separate phenomena or separate regions of activity, blend (Mol & Law, 1994). Physical computing and 3D printing are becoming part of our practice. We can combine electronic, programmable circuitry into traditional crafts such as sewing or origami. Makerspaces are difficult to define because each one is unique, fitting on a continuum of formal to informal and offering different levels of learner/participant control. For example, in some makerspaces facilitators explicitly guide projects; other makerspaces may be gatherings of individuals working on different projects without any discernible leadership. Gatherings may be physical, virtual, or both. The projects, people, and problems may lead to differing degrees of collaboration, sharing and problem solving. We argue that the activities that occur at a given makerspace emerge from the unique characteristics of the space, participants, materials, and networking practices. From a sociomaterial perspective, makerspaces may be viewed as complex assemblages in which the human, digital, and physical are highly entangled. In this paper, we describe a single phase of a larger research project examining the experiences of makerspace facilitators. Our main goal in this phase of the research was to examine the extent to which curating, creating, relating, and networking, as per the makerspace activity (MAP) diagram (Figure 1), are part of the makerspace assemblages described to us by our study participants. For this research, we conducted semi-structured interviews with 13 makerspace facilitators. The participants included teachers, librarians, school technology consultants, and makerspace club members. Our first pass at coding the transcripts resulted in a significant number of codes emerging in the relate category in comparison to the create, curate, and networking categories. This result led us to question the centrality of networking and whether or not relating should be considered the central characteristic of makerspace assemblages. We conclude that networking, while less prevalent in the transcripts (i.e., less salient to our interview participants), remains a significant characteristic. However, we offer a revised version of the MAP diagram in order to recognize the significance of relational learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".