Strengthening Statistics Graduate Programs with Statistical Collaboration - The Case of Hawassa University, Ethiopia
Bibliographic record
Abstract
This paper describes the experiences gained from the established statistical collaboration canter at Hawassa University in May 2015 as part of LISA 2020 network. The center has got similar setup as LISA of Virginia Tech. Statisticians are trained on how to become more effective scientific collaborators with researchers. The service has been delivered from May 2015 to June 2016. The University has a well established and strong academic graduate programs of statistics. The master programs are: Applied Statistics, and Mathematical and Statistical Modelling launched in 2008 and 2010, respectively. They are research based studies. The programs have produced about one hundred ninety graduates to-date, with current enrollment of over fifty students. The doctoral program started in 2013 with enrollment of ten students. The graduate students are the main role players as statistical collaborators at the center. The collaborators and clients have revealed positive feedback about the services. It is observed that the collaboration scheme seem works well and to have an impact on research quality of the non-statistician researchers. The role of statisticians is found to be important in the scientific researches of the University so as to meet the societal needs. The collaboration practice has a potential to enhance the statistics education and research at the department itself. The center needs to be strengthened and expanded for greater services and sharing experiences with other similar higher institutions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".