Bibliographic record
Abstract
In December 2007 The Wildlife Society (TWS) Ad Hoc Committee on Collegiate Programs was established and charged with assessing current wildlife programs in North America, their change over time, theory vs. practice and the role of hands‐on training, the importance of TWS certification, and what the ‘ideal’ wildlife program should look like. This task was driven by concerns over wildlife programs, the type of education that students are receiving, the quality and experience of students entering the job market, university enrollments, and the role of TWS certification. I present some of the results from our Ad Hoc Committee including a summary of our inventory of current programs in the US and Canada and some of the disturbing trends that are apparent. Nearly 4 times more programs offer wildlife courses than was previously thought, many claiming to provide the training needed for wildlife careers. Today’s programs exhibit regional differences in the kinds of curricula offered. In the Northeast, 57% of programs offering wildlife courses were in Environmental Sciences followed by the West, Southeast, and North Central all with about 30% Environmental Science programs. While in other regions Environmental programs represented <18% of what was available. In the Central Mountains and Plains, Wildlife and Fisheries programs still represented 62% of offerings, followed by the Southwest (56%) and North Central (53%). Wildlife programs represented only 29% of available programs in the Northeast. Follow‐up sampling of respondents with wildlife programs addressed questions of curricula change and student competencies. Disappearing natural history classes, declines in expensive field classes, and less hands‐on experiences for students are all disturbing trends. Simultaneously, employers are pressing for additional new skills and advanced technologies: computer skills, GIS, and enhanced communication skills. While, to encourage parents to pay for college education, many US schools have instituted credit hour limits for degrees and increased the credit hours required for the ‘general education’ core that all students must take. For example, in one traditional program, the program can include no more than 120 semester credits, of which 63 are general education. This leaves only 57 credits (19 lecture or 14 lab classes) left to cover all the skills our profession desires: from mammalogy, plant ID, and ecology to wildlife management, population modeling, statistics, and GIS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".