Making as Storytelling: Using Draw-and-Write and Object Elicitation in the Design and Study of a Library Makerspace
Bibliographic record
Abstract
In 2016, we set out to build and then study an academic makerspace. To do that, we relied on two visual research methods. The first method, draw-andwrite, served as the core of our community consultation. Because we were building a makerspace, we wanted a consultation method that was fittingly creative and visual and one that would inspire participation. Draw-and-write gave us rich output and that makerspace fit. Once the makerspace was built and running, our second method, object elicitation, helped focus interviews with research participants. Many of the participants made objects and used the process of making to tell stories, and we wanted a method that allowed those stories to be told. Object elicitation was an opportunity to let participants tell the stories of their creations and base the narrative of their experiences around the experience of making. This chapter describes how we used these two visual research methods and our reflections on doing so. Background We conducted our research at the Semaphore Studio307 Makerspace at the Faculty of Information, University of Toronto, from 2016 to 2017. For simplicity, we may refer to our field site as ‘the space’ or ‘Studio307’, as it was so often referred to by us and the participants. Field Site Studio307 is an approximately 300-square-foot, student-run makerspace located in a library school (figures 5.1 and 5.2 on the next page). The space is available during open hours for special event programming and by key access. Semaphore Research Cluster, TechFund (a student fund) and the Inforum (the library within the faculty) provided funding and equipment; the faculty itself provided the physical space and institutional support. The space's intended audience are graduate students from the Faculty of Information. It was pitched to the faculty as a place to pursue course work for classes like physical computing and mount making, to learn makerspace skills (particularly as makerspaces are becoming more common in libraries and museums) and to explore new information technologies in a hands-on manner that may not be possible in the classroom. The focus of Studio307 is small-scale fabrication, critical making, exhibition construction and skills development for current and future librarians and museum and information professionals.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| 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".