Bibliographic record
Abstract
Despite periodic debate implying that modern veterinary graduates are less competent than their predecessors, analysis of educational inputs and learning outcomes suggests that they continue to qualify with an excellent knowledge and skill set. However, increased public expectations of veterinarians have led to the need for better-designed, more integrated curricula with increased attention to communication and other professional skills and to elements of individual specialization. The need for revision of curricular content will continue. A more overriding reason for reducing content, however, is the effect this has on students' learning. Content overload in all disciplines leads to a superficial acquisition of facts, which overwhelms any drive toward understanding and extracting meaning. Unfortunately, many modern assessment methods permit replication to masquerade as problem solving, leading to short-term gains in grades at the cost of the development of information sourcing and application and other lifelong learning skills. All involved in education must be clear that our task is to develop the independent professional person. Such a person is much more than the possessor of a collection of facts and a set of individual competences. To facilitate the development of this overall capability, educators must pay as much attention to students' engagement in the learning process, and to how they understand and make meaning of our discipline, as to the specific scientific and species content of their school's individual degree programs.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".