Networking in the osteoarthritis research community in the digital era
Bibliographic record
Abstract
Networking is a vital skill for osteoarthritis (OA) researchers, offering pathways to collaboration, visibility, and career development. Recognising the evolving nature of networking in the digital era, the OARSI Early Career Investigator (ECI) Committee hosted a workshop at the 2025 OARSI World Congress titled "Navigating Networking: Strategies for Connecting Across Cultures and Personalities." This editorial summarises key insights from the session's expert presentations and panel discussion. Dr Jocelyn Bowden highlighted the enduring value of face-to-face networking for rapport-building, encouraging ECIs to approach interactions with curiosity and preparation. Dr Jackie Whittaker explored virtual networking and personality-based strategies, noting that aligning communication methods with individual preferences can enhance engagement. She stressed that there is no one-size-fits-all approach. Professor Tobias Winkler provided guidance on industry networking, underscoring the importance of understanding corporate decision-making processes and collaboration dynamics. The session closed with reflections on cultural competence and the role of authenticity in communication. While digital tools may support networking, the speakers agreed that genuine, personalised interactions remain essential. Across all formats, researchers were encouraged to build inclusive, diverse networks grounded in sincerity and mutual respect. For those unable to attend, this summary offers practical strategies to support effective networking across platforms, cultures, and career stages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".