Early-Career Coordinated Distributed Experiments: Empowerment Through Collaboration
Bibliographic record
Abstract
Abstract Coordinated distributed experiments (CDEs) enable the study of large-scale ecological patterns in geographically dispersed areas, while simultaneously providing broad academic and personal benefits for the participants. However, the effective involvement of early-career researchers (ECRs) presents major challenges. Here, we analyze the benefits and challenges of the first CDE exclusively led and conducted by ECRs (i.e. ECR-CDE), which sets a baseline for similar CDEs, and we provide recommendations for successful CDE execution. ECR-CDEs achieve most of the outcomes identified in conventional CDEs as well as extensive benefits for the young cohort of researchers, including: (i) receiving scientific credit, (ii) peer-training in new concepts and methods, (iii) developing leadership and communication skills, (iv) promoting a peer network among ECRs, and (v) building on individual engagement and independence. We also discuss the challenges of ECR-CDEs, which are mainly derived from the lack of independence and instability of the participants, and we suggest mechanisms to address them, such as resource re-allocation and communication strategies.
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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 | 0.396 | 0.464 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".