Bibliographic record
Abstract
One of the central components of research-related networked work is the exchange of advice through which researchers are expected to share useful information, especially critical information that others might not possess. A key enabler for advice exchange is the minimizing of structural constraints in the organizations. In this study, we wish to gain a better understanding of how structural constraints, in the form of social and network structure, interplay with advice exchange. Our study’s focal point is the Graphics, Animation, and New Media (GRAND) network, a national research organization in Canada. By conducting a social network survey ( N = 101), we were able to study advice giving and receiving among GRAND members. Our findings indicate that the centrality of researchers in the communication network positively correlates with both advice giving and receiving. However, the effective network size of communication networks more strongly correlates with advice giving and receiving, especially for the researchers who hold higher hierarchical positions in GRAND. Overall, our findings indicate that both the communication network and the hierarchical structure are strongly correlated with advice giving and receiving. Furthermore, by looking at the combined correlation between social and network structures with advice exchange, we can offer a better understanding of researchers’ interactions. Our findings are then discussed within the context of their potential implications for other studies on the topic of research collaboration.
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 | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".