Writing inspired by Human Library Pedagogy
Bibliographic record
Abstract
In 2016, the University of Winnipeg implemented an Indigenous course requirement (ICR) that all students need to fulfill to be able to graduate. During their degree program, they can choose any approved ICR course to fulfill this requirement. Most departments offer at least one course that has the ICR designation. I developed a course called Representations of Indigeneity in the department of Rhetoric, Writing, and Communications and have facilitated this course since then using a human library method, where we learn from real live humans that are knowledgeable about the topic. We look at a variety of themes, such as Indigenous representations in politics, business, music, art, media, land based learning, food, and research methods. To address these various topics, we had speakers into the classroom or went on fieldtrips in the area to learn more about the topic. One of the assignments in the class was for students to respond to the speakers and fieldtrips in their reflection journals where they were encouraged to be creative in their responses. This article is a collection of student responses about their interactions with the guest speakers, the fieldtrips we went on, and the class discussions we had.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".