Teaching Post-Secondary Students in Ecology and Evolution: Strategies for Early-Career Researchers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teaching can be a rewarding, yet challenging, experience for early career researchers (ECRs) in fields like ecology and evolution. Much of this challenge arises from the reality that ECRs in ecology and evolution typically receive little, if any, pedagogical training or advice on how to balance teaching, research (which can include extended field work), and other demands on their time. Here, we aim to provide accessible, pragmatic advice for ECRs in ecology and evolution who are given the opportunity to teach (as instructor of record). The advice is based on the authors’ collective experiences teaching in ecology and evolution as ECRs and is meant to help ECRs address two challenges: a) balancing the demands of teaching against one’s research, service, and personal life, and b) being effective in the classroom while doing so. The guidance we provide includes practical steps to take when teaching for the first time, including carefully refining the syllabus (course planning), adopting ‘non-traditional’ teaching methods, and taking advantage of free teaching resources. We also discuss a range of ‘soft skills’ to consider including guarding against imposter syndrome (i.e., self-doubt and fear of being exposed as a fraud), managing expectations, being empathetic, compassionate, authentic, and fostering an inclusive classroom. Lastly, we emphasize the need to focus on developing students’ critical thinking skills, integrating research and teaching where possible, and setting limits on class preparation time to maintain balance with your research and personal life. Collectively, we hope the examples provided herein offer a useful guide to ECRs new to teaching.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it