SOSCIP: supporting partnerships and innovation in advanced computing & big data analytics
Bibliographic record
Abstract
SOSCIP is a collaborative research and development (R&D) consortium established in April 2012 with IBM Canada Ltd. (IBM) as our founding and lead industrial partner. Through significant funding from the federal government [from the Federal Economic Development Agency for Southern Ontario (FedDev)], the Province of Ontario, IBM and others, SOSCIP pairs academic and industry researchers with Canada's foremost advanced computing platforms to fuel Canadian innovation leadership within the areas of agile computing, health, water, energy, cities, mining, advanced manufacturing, digital media and cybersecurity. Current consortium members include Carleton University, McMaster University, OCAD University, Queen's University (Queen's), Ryerson University, Seneca College, University of Guelph, University of Ontario Institute of Technology, University of Ottawa, University of Toronto (UofT), University of Waterloo, University of Windsor, Western University (Western), Wilfrid Laurier University, York University, Ontario Centres of Excellence (OCE) and IBM.
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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".