Toward Conceptualizing Student Outcomes in Undergraduate Field Programs and Employer Expectations for Field Positions
Bibliographic record
Abstract
Abstract A key outcome of an effective undergraduate experience is for students to gain disciplinary knowledge and develop a range of skills and habits of mind that prepare them for career success. In 2018, the Ecological Society of America (ESA) entered into an agreement with the US Geological Survey (USGS) to recruit students and recent graduates and place them into field ecology/biology internship positions at research sites across the United States. This paper focuses on field research jobs and presents a literature review to gain an understanding of workforce development issues and preliminary perspectives on critical skills in which employers might be interested. This paper is based on a case study of one employer (USGS) and investigates the potential alignment between employer expectations, field training, and perceived student outcomes. To conceptualize new graduates’ readiness and employer expectations, we identified a set of skill categories sought by field research sites. We did this by analyzing the job descriptions available through the 2019 ESA/USGS Cooperative Summer Internship Program and prior literature. These categories were incorporated into a new post‐internship student survey that was used to collect data from students. We found a potential gap in some transferable skills in both fieldwork and office work expected by employers and those offered by undergraduate field training programs. This could be explored in future studies involving a more extensive set of employers who seek to hire students with field ecology/biology skills.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".