Triggers and strategies related to the collaborative information-seeking behaviour of researchers in ResearchGate
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the triggers, strategies and outcomes of collaborative information-seeking behaviours of researchers on the ResearchGate social networking site. Design/methodology/approach Data were collected from the population of researchers who use ResearchGate. The sample was limited to the Ph.D. students and assistant professors in the library and information science domain. Qualitative interviews were used for data collection. Findings Based on the findings of the study, informal communications and complex information needs lead to a decision to use collaborative information-seeking behaviour. Also, easy access to sources of information and finding relevant information were the major positive factors contributing to collaborative information-seeking behaviour of the ResearchGate users. Users moved from collaborative Q&A strategies to sharing information, synthesising information and networking strategies based on their needs. Analysis of information-seeking behaviour showed that ResearchGate users bridged the information gap by internalizing new knowledge, making collaborative decisions and increasing their work's visibility. Originality/value As one of the initial studies on the collaborative information-seeking behaviour of ResearchGate users, this study provides a holistic picture of different triggers that affect researchers' information-seeking on ResearchGate.
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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 | 0.017 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".