Finding the Path to a Successful Graduate and Research Career: Advice for Early Career Researchers
Bibliographic record
Abstract
Abstract The path to a successful graduate and research career is a complex and difficult one. Early career researchers (ECRs) have myriad choices and tasks to prioritize and complete as they build their CV but are often confronted with unfamiliar situations in which advice from more senior researchers can be extremely valuable. Here, we summarize a recent workshop held for ECRs by the Canadian Aquatic Resource Section of the American Fisheries Society (AFS) with support from the Education Section. Sessions touched on (1) getting published, (2) science communication and outreach, (3) scoring a job or grad school position, and (4) working within the science–policy interface. The decades of collective experience brought to the table should be shared with the broader readership of AFS because it may prove useful to ECRs as well as stimulate meaningful conversations on these important and timely issues. El camino hacia una graduación exitosa y una carrera en la investigación es complejo y difícil. Los investigadores incipientes (II; aquellos que se encuentran en las primeras etapas de su carrera) tienen ante sí una miríada de opciones y retos que deben priorizar y completar a medida que construyen su CV, sin embargo suelen enfrentarse a situaciones poco familiares en las cuales el consejo de investigadores más experimentados puede resultar muy valioso. En este artículo se resume un taller de trabajo llevado a cabo recientemente para los II por parte de la sección de Recursos Acuáticos de Canadá, de la Sociedad Americana de Pesquerías (SAP), con la colaboración de la Sección de Educación. Las sesiones trataron de 1) publicación; 2) extensión y comunicación de la ciencia; 3) conseguir un trabajo o una posición en una escuela; y 4) trabajar en la interface ciencia-políticas públicas. Las décadas de experiencia colectiva puestas sobre la mesa de discusión debieran compartirse con un público más amplio de la SAP, dado que pudiera ser útil para los II así como también para estimular conversaciones productivas en estos temas de actualidad.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.064 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.092 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".