Aligning records management and risk management with business processes: a case study of Moi University in Kenya
Bibliographic record
Abstract
This paper is a synopsis of the preliminary findings of a Master of Philosophy Degree in In-formation Sciences which sought to investigate the alignment of risk management and re-cords management with business processes in Moi University with a view to proposing a strategy to enhance business performance. The study sought to undertake a business process analysis of Moi University in order to identify the records generated. It also sought to assess the current state of records management and risk management at the institution. The study was based upon the records continuum model advocated by Frank Upward (1980) and the Government of Canada Integrated Risk Management Model (2000). Preliminary findings in-dicate that poor records management practices at Moi University have been a source of risks at the institution, leading to inefficiency in business processes. The study recommends the adoption of comprehensive records management and risk management programmes and that the records-cum-risk management model proposed by the study should be adapted to aid in the implementation of these programmes. Keywords: Records management; risk management; business processes, Moi University
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".