Implementing a Current Research Information System (CRIS) in Canada
Bibliographic record
Abstract
The practice of research information management (RIM) is becoming more important as the research environment becomes increasingly complex, competitive and globalized. National mandates and requirements of national funding agencies regarding open access and research data management are creating added incentives for universities to showcase their publications and make them available in an open access format. Libraries are well situated to offer expertise throughout the adoption of a research information management system by a university. In aligning themselves with the wider strategic plans of the institution, libraries can use this as a platform to further their own goals and communicate their value and place in the institution by championing open access, ensuring discoverability and supporting the researcher endeavour. Dalhousie University is in the process of implementing a Research Information System (RIS) with the goal of providing a number of benefits to the university and its researchers. RIS serve to aid researchers when applying to funding agencies by creating consistent, standardized CVs, decrease workload when generating annual reports, increase the visibility and discoverability of an institution to potential collaborators and research contacts, augment the research currently being performed at an institution and make it more widely available, and manage and measure the research impact of individual researchers and institutions. While some challenges exist at Dalhousie that require mitigation and attention, the institution stands to benefit greatly from the implementation of this system.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".