Bibliographic record
Abstract
A Review of: Tran, N. Y., & Chan, E. K. (2020). Seeking and finding research collaborators: An exploratory study of librarian motivations, strategies, and success rates. College & Research Libraries, 81(7), 1095. https://doi.org/10.5860/crl.81.7.1095 Abstract Objective – To explore research collaboration among librarians, including librarians’ motivations for collaboration, methods for finding collaborators, and how they perceive the success of these methods. Design – Online survey questionnaire. Setting – N/A Subjects – A total of 412 librarians took the survey, and 277 respondents completed the entire survey. Methods – The researchers developed a survey using Qualtrics, including questions focused on whether respondents had sought research collaboration, factors that motivated them to collaborate, methods they used for finding collaborators, and success rates of these methods. Demographic questions were also included. Main Results – The survey results indicated that librarians are very interested in research collaboration, with 91.8% of respondents answering that they had sought collaborators, were currently collaborating, or were interested in seeking collaborators in the future. The top motivating factor for seeking collaboration was to gain expertise that the respondent lacked. The most common strategy for finding collaborators was through a respondent’s current or past place of employment, and this method was rated as extremely successful by more than 50% of respondents. Demographically, 70.1% of respondents worked in academic libraries. Conclusion – The results of this study indicate that research collaboration is of interest to librarians at a higher rate than previously observed. These results can help inform initiatives to support and promote collaboration in library and information science research, as well as provide a groundwork for further research in this area.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.023 |
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".