Overcome imposter syndrome: Contribute to working groups and build strong networks
Bibliographic record
Abstract
Scientific working groups bring together experts from different disciplines and perspectives to tackle the “wicked problems” facing natural systems and society. Yet participants can feel overwhelmed or inadequate in groups within academic environments, which tends to be most acute at early career stages and in people from systematically marginalized backgrounds. Such feelings can block innovation that would otherwise arise from gaining the full spectrum of unique perspectives, knowledge and skills from a group. Drawing on personal experiences and relevant literature, we identify ten contribution strategies, ranging from generating ideas, analyzing data, and producing visuals to supporting facilitation. Next, we share approaches for an inclusive and supportive process, considering the roles of both participants and leads. Generating the most productive and relevant outcomes from working groups requires engaging the full team in a constructive and supportive environment. We advocate that adopting inclusive approaches that respect the diversity of personality types and perspectives will lead to more innovative solutions to achieve conservation and sustainability goals.
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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 | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".