Information professionals' participation in interdisciplinary research: a preliminary study of factors affecting successful collaborations
Bibliographic record
Abstract
OBJECTIVES: This pilot study explores the conditions that support or hinder information professionals' participation in interdisciplinary research teams. METHODS: We undertook a preliminary grounded theory study investigating factors that impact on information professionals' participation in interdisciplinary research. Four biomedical information professionals working in academic universities and teaching hospitals in Canada participated in semi-structured interviews. Grounded theory methods guided the data collection and analysis. RESULTS: Participants identified the conditions that support or hinder research participation as belonging to four distinct overlapping domains: client-level factors including preconceptions and researcher resistance; individual-level factors such as research readiness; opportunities that are most often made not found; and organisational supports. CONCLUSIONS: Creating willingness, building preparedness and capitalising on opportunity appear crucial to successful participation in interdisciplinary research. Further exploration of the importance of educational, collegial and organisational supports may reveal additional data to support the development of a grounded theory regarding the facilitation of information professionals' engagement in interdisciplinary research.
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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 | 0.039 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".