A case for low-stakes collaboration: Increasing access through a mini-assignment in the first-year composition classroom
Bibliographic record
Abstract
This article makes the case for collaboration between first-year composition classrooms and academic libraries through a low-stakes mini-assignment that incentivizes student use of existing library resources. I present and analyze data from 168 “mini-assignments” in which students must use one of the existing mediated library services available to them in the course of writing their research essay (use the chat help function; book an appointment with a librarian; email a question to a librarian; text a question to a librarian; or visit the main desk in the library). Students need to describe what service they used and what kind of help they asked for and then reflect on the help-seeking process including how they felt before, during, and after seeking help, and what they would do the same or differently next time. I argue that this low-stakes assignment positively impacts students' future use, future confidence, and also ‘levels up’ the kind of engagement that students are comfortable undertaking. This mini-assignment, then, benefits first-year students and particularly equity-deserving and at-risk students, by making explicit how to get help from the university library as part of the first-year academic writing classroom, and showcasing librarians as co-educators with pedagogical expertise.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".