Factors and outcomes of collaborative information monitoring
Bibliographic record
Abstract
BackgroundKeeping up to date with scientific literature is intrinsic to research but remains challenging due to information overload, time constraints, and despite existing tools (e.g., search alerts).It is particularly challenging for researchers in multidisciplinary fields, such as Patient Oriented Research (POR), who need to cast their nets wide to identify relevant studies.Collaborative information monitoring, or sharing the monitoring effort among group members, may be a solution.Indeed, collaboration is intrinsic to research and peers are often preferred sources for keeping up to date.Collaboration is also known to have the potential to solve complex problems and lead to knowledge discovery.Yet, some knowledge gaps remain.While recognized as important, most studies focus on active searching rather than monitoring.Most studies investigate individual rather than collaborative behaviour.More research is needed to understand the experiences and outcomes of collaboration, and to bridge collaborative information seeking with collaborative information monitoring.This research presents original contributions to knowledge on collaborative information seeking and monitoring.It offers actionable recommendations valuable for implementing, supporting, and evaluating collaborative information projects, potentially helping POR stakeholders and researchers in other fields keep up to date collaboratively.
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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 | 0.018 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".