“Made-in-Canada ” Options for Improving Integration
Bibliographic record
Abstract
In this article, the authors describe relatively recent efforts by scientific research agencies to promote, through various funding programs, the integration of social sciences and humanities with the natural sciences. This “integrated ” approach seeks to study science through a broader interdisciplinary lens in order to better anticipate, understand, and address its ethical, legal, and social implications. The authors review the origins and evolution of this trend, as well the arguments which have been formulated by both proponents and critics of integration. By using Genome Canada’s “GE3LS” Research Program as a case study, the authors discuss the successes and continuing challenges of this model based on evaluation results available to date. The authors then go on to examine and compare three possible models for improving the future success of the GE3LS research program, including: 1) enhancing the current integrated research approach through incremental refinements based on concrete evidence and lessons learned; 2) promoting greater interaction and synergy across GE3LS research projects through a deliberate, systematic and coordinated “hub and spoke ” approach; and 3) taking a broad programmatic approach to GE3LS research by creating a central resource of available expertise and advisory capacity.
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 | 0.082 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".