Making a Third Space for Student Voices in Two Academic Libraries
Bibliographic record
Abstract
When we think of voices in the library, we have tended to think of them as disruptive, something to control and manage for the sake of the total library environment. The stereotype of the shushing librarian pervades public perception, creating expectations about the kinds of spaces libraries want to create. Voices are not always disruptive, however. Indeed, developing an academic voice is one of the main challenges facing incoming university students, and libraries can play an important role in helping these students find their academic voices. Two initiatives at two different academic libraries are explored here: a Secrets Wall, where students are invited to write and share a secret during exam time while seeing, reading, commenting on the secrets of others; and a librarian and historian team-taught course called History on the Web, which brings together information literacy and the study of history in the digital age. This article examines both projects and considers how critical perspectives on voice and identity might guide our instructional practices, helping students to learn to write themselves into the university. Further, it describes how both the Secrets Wall and the History on the Web projects intentionally create a kind of “Third Space” designed specifically so students can enter it, negotiate with it, interrogate it, and eventually come to be part of it.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.043 | 0.024 |
| Scholarly communication | 0.041 | 0.021 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".