Bibliographic record
Abstract
As we plan our future in the twenty-first century, many believe that we face more problems than ever before, including the rising cost of sustaining teaching, research, and service programs in a climate in which state support for higher education is declining. Some commonly held opinions blame leaders and thus propose solutions that are based on the premise that leaders who are perceived to be ineffective should be replaced by those who promise to correct the situation. Leadership is a frequently discussed term, whereas the concept of followership is generally ignored. Followership, however, has been an unidentified facet of leadership in veterinary academia. The present article examines the premise that the primary way to solve the expanding list of problems facing academia is by zealously seeking, teaching, and encouraging leadership. Organizations such as universities succeed or fail on the basis of how well followers follow in addition to how well leaders lead. The truth is that without followers there would be no leaders. Yet the train of followers is almost nonexistent in most educational settings. Striving to recruit and entertain the proper balance of followers and leaders should be one of the goals of every college of veterinary medicine.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".