Bibliographic record
Abstract
At the end of October 2001, I had the pleasure of participating in the CVMA's Public Policy Advocacy Seminar in Ottawa. Representatives from 8 provincial veterinary associations along with the CVMA Executive and Committee chairs were in attendance. The weekend's stated objective was to help to train a number of individuals involved in veterinary association work to make effective representation to government decision makers and public opinion leaders. Educating the public and governments to the importance of the veterinary profession's contributions to society, while at the same time defending the profession's position against the erosion of veterinary services, is paramount, if we are to prosper as a profession. By all accounts, the seminar was successful as a first step in helping provincial associations to deal with the lack of formal training in public policy advocacy. Nationally, the CVMA's public policy advocacy objective is to promote the interests, priorities, and opinions of Canadian veterinarians whose mission, through successful private, public, and academic practices, is to enhance animal health, public health, and animal welfare. The CVMA has become much more proactive in forging closer ties with the various federal government departments and agencies. With the valuable assistance of Dr. Gordon Dittberner, CVMA Senior Advisor, Veterinary Affairs, the Association has increased contacts with the Canadian Food Inspection Agency, the Veterinary Drugs Directorate, Agriculture and Agri-Food Canada, and other relevant government departments. The CVMA is also a participant in the Canadian Animal Health Coalition, the Canadian Animal Health Consultative Committee, and a number of national industry committees. The goal of the CVMA's increased interactions with governments and other like-minded groups is to position itself as the independent advisor of choice on all veterinary issues to ministers, other federal politicians, public servants, and industry associations. Since the best way to predict the future is to create it, the CVMA's input in the decision making process will hopefully ensure that events unfold as the profession thinks they should.
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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.043 | 0.017 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.052 | 0.045 |
| Insufficient payload (model declined to judge) | 0.052 | 0.017 |
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".