First CINP Research Fellowship for Early Careers: Bridging Scientific Goals and Professional Networking
Bibliographic record
Abstract
Participating in scientific conferences and professional networking support productivity in research. However, there are barriers for early career scientists to benefit from these activities. In 2023, the International College of Neuropsychopharmacology (CINP) Committee for Early Careers organised the first edition of the CINP Research Fellowship for Early Careers to support international collaboration of junior neuroscientists and facilitate in-person exchange between early career and senior investigators. The programme included online and in-person sessions, the latter during the 34th CINP World Congress in Montreal. Selected fellows had the opportunity to learn and make round-table discussions with renowned scientists, including Professors Paola Dazzan, Alan Frazer, Gabriella Gobbi, Anthony Grace, Oliver Howes, Kazutaka Ikeda, Kazuyuki Nakagome, Maria Oquendo, Dan Rujescu, Eric Vermetten, and Joseph Zohar, enabling early career researchers to understand each mentor’s main scientific trajectory and research methodology. The underpinning aim to support the global networking of early career researchers was achieved through the programme, as evidenced by the ensuing collaborative projects.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".