How Far can Scholarly Networks Go? Examining the Relationships between Distance, Disciplines, Motivations, and Clusters
Bibliographic record
Abstract
Abstract This study aims to understand the extent to which scholarly networks are connected both in person and through information and communication technologies, and in particular, how distance, disciplines, and motivations for participating in these networks interplay with the clusters they form. The focal point for our analysis is the Graphics, Animation and New Media Network of Centres of Excellence (GRAND NCE), a Canadian scholarly network in which scholars collaborate across disciplinary, institutional, and geographical boundaries in one or multiple projects with the aid of information and communication technologies. To understand the complexity in such networks, we first identified scholars’ clusters within the work, want-to-meet, and help networks of GRAND and examined the correlation between these clusters as well as with disciplines and geographic locations. We then identified three types of motivation that drove scholars to join GRAND: practical issues, novelty-exploration, and networking. Our findings indicate that (1) scholars’ interests in the networking opportunities provided by GRAND may not easily translate into actual interactions. Although scholars express interests in boundary-spanning collaborations, these mostly occur within the same discipline and geographic area. (2) Some motivations are reflected in the structural characteristics of the clusters we identify, while others are irrelevant to the establishment of collaborative ties. We argue that institutional intervention may be used to enhance geographically dispersed, multidisciplinary collaboration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".