Factors and outcomes of collaborative information seeking: A mixed studies review with a framework synthesis
Bibliographic record
Abstract
Abstract Despite being necessary, keeping up to date with new information and trends remains challenging in many fields due to information overload, time constraints, and insufficient evaluation skills. Collaboration, or sharing the effort among group members, may be a solution, but more knowledge is needed. To guide future research on the potential role of collaboration in keeping up to date, we conducted a systematic literature review with a framework synthesis aimed to adapt the conceptual framework for environmental scanning to a collaborative context. Our specific objectives were to identify the factors and outcomes of collaborative information seeking (CIS) and use them to propose an adapted conceptual framework. Fifty‐one empirical studies were included and synthesized using a hybrid thematic synthesis. The adapted framework includes seven types of influencing factors and five types of outcomes. Our review contributes to the theoretical expansion of knowledge on CIS in general and provides a conceptual framework to study collaboration in keeping up to date. Overall, our findings will be useful to researchers, practitioners, team leaders, and system designers implementing and evaluating collaborative information projects.
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 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 | 0.044 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".