Communication is the foundation of an impactful and resilient scientific community
Bibliographic record
Abstract
Scientific communities play a critical role in the structure and process of science. Creating and sustaining these communities relies on clear channels of communication to enable the effective and inclusive exchange of ideas, norms, and practices. “Science communication” is therefore a critical part of the overall research ecosystem, but one that is traditionally undervalued. While popular uses of the term primarily focus on communicating science to lay audiences, science communication among peers also plays a crucial role in the growth and maintenance of professional scientific communities, such as the Organization for Human Brain Mapping (OHBM). Based on our experiences as Chairs of the OHBM Communications Committee, we argue that effective science communication is critical to building and sustaining an impactful, inclusive, and resilient scientific community. We highlight how mediums including the OHBM podcast, blog, and newsletter support and strengthen its parent community, while also fostering connections with adjacent communities with overlapping specializations such as the International Society for Magnetic Resonance in Medicine (ISMRM) and field-wide neuroscience efforts such as The Transmitter. We argue that science communication extends well beyond traditional publications and improves the research process and outcomes, both for individual researchers as well as the field more broadly.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.027 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".