Bibliographic record
Abstract
Abstract Since 2005, Michigan State University has offered an online graduate-level course in animal welfare assessment. The course was designed to overcome geographic barriers in terms of limited instructor expertise in welfare at individual universities and to reach an international student audience. Over 280 students have taken the course—including undergraduate, veterinary, and graduate students; practicing veterinarians; and professionals. Students have attended from 22 states and 13 different countries, including Thailand, Canada, Nepal, Kenya, China, and Australia. The course was designed and continues to be modified to accommodate students attending from different time zones, with different types of technology, and with different speeds and reliabilities of Internet access. An asynchronous format is used, with content delivered via short, recorded lectures and videos as well as electronically available textbook chapters and journal articles. In response to student and instructor needs, the course has evolved from a tech heavy but bandwidth intensive format to one using simpler technology and less bandwidth to reduce student barriers to participate and ensure equitable content access and engagement. Content is offered through the Desire 2 Learn course management system at MSU, which also performs accessibility checks of content. Lectures and videos are captioned to help accommodate visually impaired students or those in noisy environments and PDF handout versions of lecture slides are also made available. Rubrics and examples are used to guide completion of assignments. In 2020, the course was co-taught by MSU, Texas A&M and Virginia Tech. Several class meetings were held via Zoom to allow students to meet and interact with each other, though issues with scheduling prohibited all students from attending. Feedback surveys indicate students are generally satisfied with course content, delivery technology and interactions with instructors and peers. Creating an inclusive virtual environment requires attention to student constraints, with simpler typically being more accommodating.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.027 |
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".