University of British Columbia: What does a university look like?
Bibliographic record
Abstract
University of British Columbia About the Researchers The network above represents a connected graph of 23,313 co-authored researchers affiliated to the University of British Columbia from 2017-2022, making up 93% of all affiliated researchers over this time period. Each researcher has been colour coded by the 2-digit FoR 2020 code they are most associated with. Each researcher is depicted by a sphere, and given a size based on the number of publications produced. About the Clusters 266 research clusters were identified in the network above. To make the network easier to read, collaborations between clusters are not displayed, although they do play a significant role in the layout of the network. Clusters of 20 or more researchers can be explored further in the associated figshare record (linked in the QR code top right of legend). Clusters are colour coded by the most dominant discipline of the researchers within them, and are given a ‘height' based on the discipline that they proportionally belong to. Biomedical and Clinical Sciences clusters sit at the base of the network, with Language, Communication and Culture sitting at the top. About the Classifications The 2020 Field of Research codes used in this analysis have been assigned to publications using the approach detailed in “Recategorising research: Mapping from FoR 2008 to FoR 2020 in Dimensions” (https://doi.org/10.1162/qss_a_00244.) Note: some research areas are not well represented in the network due to single author publications. Fields of Research with greater than 50% of their output not represented in the network include: Commerce, Management, Tourism and Services (51.56%), Economics (63.71%), Philosophy and Religious Studies (65.34%), History, Heritage and Archaeology (75.77%), Creative Arts and Writing (76.05%), and Language, Communication and Culture (77.54%) Methodology: Graph layout: Batchlayout [1] Clustering: Leiden Algorithm [2] 3d Layout: Blender [3] Data: Dimensions [4]
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".