A 1-Day Immersion in Professional Development for Early Career Faculty and Researchers
Bibliographic record
Abstract
Abstract The early career stage for scientific researchers and faculty is fraught with challenges, including establishing professional relationships, securing funding, balancing work and personal life, and navigating job uncertainties. Early career professionals were among those especially impacted by the COVID-19 pandemic, in having limited opportunities for networking and professional development. Recognizing these challenges, the University Corporation for Atmospheric Research (UCAR) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) piloted a 1-day professional development workshop in Boulder, Colorado, on 8 October 2023 as a preworkshop for the biannual UCAR Members Meeting. We hoped to foster networking and peer learning among 122 attendees from the atmospheric sciences, 76 (62%) of whom were university faculty and 46 (38%) of whom were postdocs, researchers, and staff from NSF NCAR and UCAR. Participants, representing 58 universities across the U.S. and Canada, engaged in a program that included networking events, informational sessions, and hands-on workshops. Sessions covered topics such as active learning, mentoring graduate students, understanding tenure, time management, mental health, fostering welcoming environments, and grant proposal writing. Feedback from participants highlighted the value of networking opportunities and peer learning, emphasizing the importance of continued professional development tailored to early career scientists’ needs. The workshop also allowed us to learn more about challenges early career professionals are facing. This workshop serves as a model for future initiatives aimed at supporting early career researchers in Earth system science and related fields.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".