Bibliographic record
Abstract
This podcast is an interview between Outstanding Scholar Student Laila Albalkhi and Outstanding Scholar Coordinator Tim Brunet. Learn about the practice of auto ethnography and why Outstanding Scholars will have an opportunity to do research while on exchange. Laila Albalkhi, Deborah Laze, and Samantha Blackwell have begun constructing autoethnography exercises and resources. No autoethnographies will begin until it received Research Ethics Board clearance. Abstract: This paper provides both pedagogical exercises and an autoethnography methodology for university students on exchange. This project was developed with the Coordinator of the Outstanding Scholars program and three Outstanding Scholar Students at the University of Windsor. The Outstanding Scholars program is an elite academic program for high-achieving and community-engaged students who complete six paid research placements during their undergraduate studies. Students in the program can complete an international exchange and use this autoethnography framework for paid placementsduring their exchange. This paper introduces the practice of autoethnography, proposes research activities, provides standard operating procedures, publishes pedagogical exercises, declares research methodology, provides the project's scope, lists limitations, and suggests generative opportunities for further development. This autoethnography initiative is part of a larger pedagogical framework informed by the Capability Approach and the University of Windsor’s UWill Discover Sustainable Futures project. This paper offers vetted research activities where students think critically about their interpersonal communications before, during and after their exchange. Students can complete the assignment as a pedagogical exercise, publish their work in an agreed upon journal, or develop new exercises for the project. The paper includes sample exercises for students.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 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".