140 Development of an Online Educational Initiative to Engage Beef Stakeholders in a Global Scale
Bibliographic record
Abstract
Abstract The objective of this educational initiative (International Beef Cattle Academy; IBCA) was to present translational research-driven content regarding beef production to international stakeholders. The methodology for development of the IBCA program included: 1) determination of content courses (n = 7); 2) assignment of faculty members as content creators and instructors (n = 11), general coordinator (n = 1) and associate coordinators (n = 2); 3) selection of participants from applicant pool using a rubric designed to create an inclusive and diverse learning community of international industry leaders. Course structure and content delivery remained consistent across all courses. Asynchronous lectures were delivered via a LMS (D2L), and weekly, hour-long synchronous remote meetings for participants were hosted by instructors. Within courses, content was distributed to yield 3.49 ± 0.41 h of recorded material per week. Automatically graded quizzes with multiple attempts allowed, were utilized as formative assessments and mechanism for control of content release. Access to subsequent topics was granted when the quiz score was of 80% or above. At the end of each program cycle (11 months) an optional experiential learning opportunity was offered. Participants were invited to a week-long instructor supervised workshop in which they took part in hands-on practicums as well as field trips. After 3 full cycles, qualitative analysis of participant feedback given in the form of interviews (n = 19) has generated emergent themes of learning environment satisfaction and successful adoption/application of new technologies. Interaction during the live synchronous sessions and the applicability of the content were amongst the positive aspects according to participant feedback. Based on these interviews, the IBCA successfully met the objective of improving knowledge and adoption of research-based technologies through content delivery and learning community creation for beef industry stakeholders from 14 different countries (USA, Canada, Brazil, Romania, Kazakhstan, Mexico, Panama, Dominican Republic, Germany, South Africa, Zimbabwe, Turkey, Australia, Pakistan).
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".