Abstract Number ‐ 180: A Novel Internet Platform for Neurovascular Research Collaboration and Funding
Bibliographic record
Abstract
Introduction The current neurovascular research funding environment is highly competitive, stifling collaboration and hindering progress. Further, specific groups of researchers/research topics are disproportionately affected, resulting in inequity in the funding process. These include early‐career researchers, women, and researchers from low‐middle income countries. Certain conditions, e.g., rare, stroke‐related diseases are due to their low prevalence not suitable for clinical trials, and therefore even less likely to receive funding. Currently, there is no easy way for people working on such topics to come together and collaborate. To address this problem, a novel internet‐based platform, Collavidence (www.collavidence.com), was designed. The idea is to complement current systems of neurovascular research collaboration and funding for more inclusive, efficient, and impactful research results. The aim of this study is to present the initial performance of the platform in achieving this goal. Methods Analytics on pre‐defined user‐, project‐, and interaction‐based metrics will be performed to describe the level of platform engagement in the initial months following launch. These include the number of users and projects posted, the amount of funding accumulated, the proportion of successfully funded projects, and the iterative improvement of the proposals. Further, the relative engagement of early‐career, female researchers, and researchers from low‐middle‐income countries will be assessed. Results A qualitative assessment of the value of the overall platform, the process of iterative review, and possibilities for collaboration will be presented. Further, trends in platform engagement during the initial 3 months, including the relative distribution of specific user demographics to assess the platform’s success in encouraging equity, diversity, and inclusion, will be presented. Conclusions This study will assess the feasibility and initial success of Collavidence as a unique platform for neurovascular research collaboration and funding.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".