Making Things Together: Collaborating and Mentoring on an OER Project
Bibliographic record
Abstract
Making Things Together: Collaborating and Mentoring on an OER Project 2In spring 2020, during the early months of the COVID-19 pandemic, articles in the Smithsonian Magazine and the Atlantic reported a resurgence of interest in handicrafts as a means of both finding calm and building community (Grossman; Machemer; Smith).DIY arts and crafts surged in popularity in North America, with "stay-at-home orders [inspiring] those with ample free time to pick up hands-on projects" (Machemer).At the same time, teaching and learning, as well as academic conferences, moved online into hands-off virtual spaces.Connected by a shared interest in both craft and Victorian material culture, a small group of academics piloted two virtual events to enable hands-on learning in a hands-off context: a roundtable on Victorian objects and a workshop on Victorian hair art.Prompted by COVID-19 restrictions on in-person gatherings and fueled by the community support that coalesced around these events, the group launched a year-long series to study old things using new methods of virtual connection: Crafting Communities: A Series of Victorian Object Lessons & Scholarly Exchanges in COVID Times. 1 In its inception, we, the members of this group, imagined Crafting Communities primarily as a series of virtual events hosted over Zoom.But as we sought to secure a legacy for live roundtable and workshop events by developing a digital exhibit, a podcast, and a website, what we had imagined primarily as an event series morphed into a digital humanities (DH) project and an open educational resource (OER).As we assembled a team of collaborators and recruited student research assistants, our hands-on investigation of Victorian material culture became, also, a hands-on crash course in digital making, collaboration, and mentoring.We found ourselves doing what we now think of as "Accidental DH"-that is, learning about DH methods at the same time as we collaborated remotely with a geographically dispersed group of students.As we pursued our research focus on Victorian material culture and hands-on making, we discovered compelling parallels between our crafting of physical objects and the cultivation of digital legacy projects--that is, online resources and archives created to support and inspire further learning.While we had a lot to learn about the digital tools we were employing, our most valuable lessons concerned mentorship, lessons we learned from making things together as a team collaborating remotely across three provinces.This essay argues for the value of making together as a form of mentoring.In it, we explain how our project's focus on experimental crafting prompted us both to see and to appreciate connections between the processes of experimental crafting and digital making, processes which benefit alike from collaboration and peer mentorship.Our hands-on workshops exploring Victorian craft practices-which emphasized the pleasure of making things, the benefits of working together, and the value of failure as part of learning-primed us to imagine our OER project in similar terms and to focus on hands-on experimentation, peer mentorship, and acceptance of uncertainty.Faculty members on the team thus set aside their traditional roles as supervisorexperts, instead learning alongside student team members in skills-based training sessions and facilitating mentorship opportunities that often centred students as experts, inviting students to mentor the project's faculty members as well as one another.The project thus embraced a multi-directional mentoring model in which all team members had opportunities to learn, teach, and mentor.As faculty members made things together, they
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".