Understanding Faculty Collaboration and Productivity: A Case Study
Bibliographic record
Abstract
Research productivity has been always an important part of every academic’s job, since it has a profound effect on faculty promotion and tenure decisions. In addition, some scholars believe that co-authorship between faculty members has a great impact on their academic life and faculty advancement. Since 2005, the Ministry of Education of Taiwan (MOE) has developed two university programs and evaluation policies for improving the competitiveness and internationalization of Taiwan universities, and has clearly stated that there is a strong relationship between faculty promotion and research performance. However, none of them has used social network analysis (SNA) to examine research productivity and co-authorship under two university programs and evaluation policies from MOE in Taiwan. Therefore, in this study, we first uses SNA to analyze the research productivity, collaboration patterns, and publication strategies of faculty members in a Management Information Systems (MIS) department at a national university in Taiwan. Then, we used D3, a well-known drawing tool to create data visualization using JavaScript libraries, to visualize and discuss how these two university programs and evaluation policies from the MOE affected these patterns and strategies. We hope that our study not only provides beneficial information to the MIS department, but can be treated as an important source for MOE committees in their future adjustment of university programs and policies.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".