Research Activities and Networks in CAIS Conferences for the Period of 1993- 2015: Social Network Analysis
Bibliographic record
Abstract
Since 1973 the Canadian Association for Information Science (CAIS/ACSI) has consecutively held 43 annual conferences. The purpose of this study is to better understand the research and collaborative activities in the community of CAIS conferences, based on a social network analysis (SNA) approach. A total of 827 papers from 778 authors have been presented in CAIS for the period of 1993 to 2015, in association with 209 different organizations and 25 countries. A component analysis that has been applied to the collaboration network has discovered research collaboration patterns. This study contributes to discovering collaborative research activities and formation through the CAIS conference and to the literature of the scientific collaboration in the LIS field. Depuis 1973, l'Association canadienne de sciences de l'information (ACSI/CAIS) a tenu 43 congrès annuels consécutifs. Le but de cette étude est de mieux comprendre les activités de recherche et de collaboration dans la communauté de l’ACSI, à l’aide d’une approche d’analyse des réseaux sociaux (ARS). Un total de 827 articles de 778 auteurs ont été présentés à l’ACSI dans la période 1993-2015, en association avec 209 organisations différentes et 25 pays. L’analyse des composantes du réseau de collaboration met en lumière l’existence de patrons de collaboration de recherche au sein de la communauté. Cette étude contribue à l’étude des activités de collaboration au sein des congrès de l’ACSI ainsi qu’à la littérature sur la collaboration scientifique dans le domaine BSI.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.020 | 0.035 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".