Implementation of an Elective Micro-credentialling “Badging” Program in a Doctor of Veterinary Medicine Program
Bibliographic record
Abstract
Badging or micro-credentialing programs have been used in higher education to allow students to pursue additional skills training helpful to their intended career path. These programs, overseen by faculty members and tracked using digital certification software, create an incorruptible ledger of student learning and achievement that can be shared with potential employers and providers of advanced training at the student's discretion. This teaching tip describes the implementation of a voluntary badging program for students at one veterinary college, including faculty member and student engagement in the badging program during its first year of implementation. During that year, five faculty members created seven unique badges, each with three levels of achievement (bronze, silver, and gold). During the first year, 10 badges were awarded to 8 students, and numerous other students were in the process of completing badge requirements. Students reported participating in the program to gain more skill in an area of interest and because they thought the program would be beneficial in getting a job or into an advanced training program. The majority of students participating during the program's first year reported planning to continue working on badges in the next year. An additional cohort of students who did not participate in the first year also expressed interest in starting to work on badges. The badging program allowed students to document their progress in learning skills of interest. The college intends to develop additional badges in areas of high student interest and continue research into stakeholders' opinions of the badges received.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".