A business case for a data-driven decision-making tool to support the UNBC research enterprise.
Bibliographic record
Abstract
Business intelligence tools allows for data-driven decision-making within organizations using historical events to predict future trends, which is especially valuable when allocating operational resources. As a research-intensive Canadian university, UNBC has seen a significant increase in activities related to supporting the research enterprise, which requires additional resources (human, capital, financial etc.) in order to effectively and efficiently advance the mission of the research community. As outlined in our Annual University Accountability Report, 2018/19 was an incredibly productive year for research with more than $14 million received in support of research. The University has seen a significant increase in the number and breadth of agencies and organizations funding research at UNBC. The administration of research awards involves both pre-award and post-award processes, which requires responsible allocation of available resources to ensure a sustainable model will be developed to achieve goals outlined by the institution’s strategic priorities and build the foundation to reach our goal of a research enterprise generating $25M in annual research revenue. Therefore, using business intelligence tools to utilize historical data to predict the necessary resourcing needs of the institution will allow UNBC to make strategic investments in research and remain competitive on the provincial, national and international stage. Informed decision-making when investing resources are critical to the success of any business. The goal of my MBA project is to gather critical information to be used in the development a data visualization and forecasting tool that will allow for informed decisions for the allocation of resources necessary to support the research mission at UNBC. The objectives of the MBA project are two-fold, which include the development of the business case for the UNBC data visualization tool (DVT) and also the completion of a design document. The information gathered6 from this project will be used in the future (post-MBA) to develop a data visualization tool that will allow for the on-going monitoring of UNBC’s progress towards putting in place the appropriate resources to reach $25M in annual research revenue. Specifically, the MBA project will consist of completing a comprehensive business case outlining the “business need” and potential solutions. Secondly, the MBA project will consist of developing a “design document” for an eventual tool that will be used to visualize research funding and labor information to inform business decisions for resource planning for the UNBC research enterprise. This design support system will be used by senior leadership within UNBC to effectively and efficiently make decisions to allocate resources.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".