Things Fall Apart: Supporting Undergraduate Research in the Archives
Bibliographic record
Abstract
Things Fall Apart: Supporting Undergraduate Research in the Archives Laurie McNeill (bio) Just like our students, instructors teaching in archives face a steep learning curve as they navigate curriculum and assessment design that supports successful undergraduate research in the archives. Like many instructors now incorporating archival research into our courses, I am not formally trained in archival studies and, as a result, my pedagogies of the archive have involved a fair degree of reverse-engineering, as I’ve attempted to articulate research methodologies that will be accessible and adaptable by students. What are we looking for? How do we know when we’ve found it—or, more pressingly, how to find it? What are best practices for note-taking, citation, preparation, and (crucially) time-management? This (backwards) design process has unfolded over the eight years I’ve been incorporating archival studies into my literature and academic writing courses at the University of British Columbia. In this time, I’ve also come to better understand the cognate challenges archival research can present to undergraduate researchers and that teaching with archives can present for instructors. In what follows, I share my experiences in two types of courses—a first-year academic writing and literature course, in which archival studies is one module, and semester-long senior seminars for English honours and majors students. Throughout, I share my students’ [End Page 15] reflections and responses to their experiences in the archives as they articulate how the archives have shaped their learning in these contexts.1 Archives present significant challenges for novice researchers. In particular, as many instructors note, students don’t appreciate the additional time this kind of work takes, despite—as my own experience demonstrates—explicit warning on this front (Hayden 412; Johnson and Mulder 40). Many students will also be learning how to do research with primary sources in the first place, a key goal of many pedagogical uses of the archive (Johnson and Mulder 40; Dean 38–44; Fansler and Yun 15–17; Senf; Spraggs; Mutnick 376–78) and with the practices of research itself. As Deborah Mutnick notes, depending on the course and assessments, first-time archival researchers may also be grappling with “myriad difficulties of college-level reading, writing, and research” (375). The rewards, however, can also be “myriad,” with students developing transformative aptitudes in primary research and “critical information literacy” (Porterfield; Phillips and Shaw 50) and realizing opportunities to do what Carol Senf calls “genuine research” (298) that, Heather Dean argues, allows “[s]tu- dents to feel like real scholars” (42). Students can participate in “inquiry-based learning” rather than producing a “generic” research paper that is an “assembling of sources without real engagement or knowledge of them” (Mutnick 383), resulting in an experience that is intellectually more profitable for instructors to evaluate as well as students to produce. In other types of assessment, they can make public contributions to knowledge (vanHaitsma 37–39; Sheffer and Hunker). As Wendy Hayden notes, “asking students to undertake the difficult task of archival research invites them into the scholarly community, where they have much to contribute” (419), with this invitation often engendering “a level of student engagement not often observed in traditional research projects” (406). Importantly for the kinds of engagement we can imagine in our courses, as Dean suggests, “learning in the archives further enriches disciplinary knowledge” (42). Extending Hayden’s argument that “the flexibility and creativity required in methods for this research lead to further pedagogical ‘gifts’ of the archives” (406), I argue that the process of working through the difficulties [End Page 16] of archives-based research surely fosters the “gifts” of resilience and growth-mindset (see, for example, Dweck), as students necessarily face setbacks and need to reflect on practice to persevere. These goals and insights are, I confess, ones that have crystallized for me only over time. In the beginning, I was motivated more simply by the desire to provide my first-year students with a range of methodological and research experiences and to take advantage of our proximity to the University of British Columbia’s Rare Books and Special Collections (rbsc). Having done archival research as part of my dissertation, I knew how...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".