Bibliographic record
Abstract
Learning outcomes The learning outcomes are as follows: How to establish credibility of data sources; measurement scales of data; the importance of descriptive statistics and generating the following based on the type of data: mean, median and standard deviation; graphical methods; and test for differences: t-test and analysis of variance. Case overview/synopsis The case is set during the COVID-19 pandemic and the South African Government’s response to the pandemic. A brief timeline is provided as part of the introduction to the case study, with the following being a timeline of the events: 14 March 2020, 114 South African citizens were repatriated from Wuhan the epicentre of the COVID-19 outbreak; 15 March 2020, South Africa’s President, Cyril Ramaphosa declares a National State of Disaster, and this includes various measures to protect against the spread of COVID-19, while the health-care system is geared up to deal with the pandemic. Among the measures implemented, travel bans from high-risk countries and closing of air-traffic, closing of land ports and banning of gatherings of more than 100 people; 23 March 2020, President Cyril Ramaphosa announced a national lockdown beginning on 27 March 2020 for three weeks; 9 April 2020, President Ramaphosa extends the national lockdown by a further two weeks. The World Health Organisation (WHO) had commended South Africa on the swift action taken to curb the spread of the virus. Individuals and organisational leaders are grappling to make sense of the spread of the virus, and the barrage of the information that is being communicated through multiple channels, formal and informal. To make sense of the information, the case is premised on getting access to the raw data and conducting the analysis based on the publicly available data. The central requirement of the case is to compare the number of positive cases per million, based on the population data contained in the data set, of South Africa to a comparable country. Complexity/Academic level Post-graduate students learning statistics as part of a degree programme. The case assumes no prior statistics knowledge and therefore is aimed at teaching the importance of the basics of statistical analysis and then progressing to tests for differences. Subject code CSS 7: Management Science Supplementary materials Teaching Notes are available for educators only.
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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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.039 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.056 | 0.017 |
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".