2010 report of veterinarians employed in Government, Industry, and Academe.
Bibliographic record
Abstract
The 2010 Survey of Veterinarians in Government, Industry and Academe (GIA) was designed to provide veterinarians who have interests outside of private practice, information upon which to benchmark their wages and benefits. The survey is provided by the Ontario Veterinary Medical Association in partnership with the Canadian Veterinary Medical Association Business Management Program with cooperation from the Canadian Animal Health Institute. The survey was sent to 699 GIA veterinarians across Canada; 197 surveys were completed and returned, for a response rate of 28%. The results are generally accurate within 4.2%, 19 times out of 20. The information in the report refers to incomes earned in 2009. In 2009, the average full-time GIA veterinarian earned $98 000 and worked 1800 hours. Annual hours stayed the same while GIA professional earnings increased 3%. Inflation over the same period was 0.3%; therefore, this subtle increase allowed for a nice bump in pay. This is even more significant considering that 2008 earnings had fallen by 3%. Since the average number of years employed in the field did not change, this increase can only mean that employers are making up for the wage cuts from 2008 (Table 1). Table 1 Earnings of veterinarians in government, industry and academe in 2008 and 2009
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".