Online newspaper reading patterns of a university community before and during the COVID-19 pandemic: the case of the University of Venda
Bibliographic record
Abstract
Rationale of Study – For years, academics have been concerned with the quality of assignments, particularly at the undergraduate level, due to low reading cultures among students. However, the reading culture of university staff members also needs to be considered, as well as how it can influence students to read for leisure, remain informed about various aspects, and incorporate it into their academic writing. This study explored the reading culture in university communities that access and read online newspapers through the PressReader Database before and during the COVID-19 pandemic at the University of Venda (UNIVEN).Methodology – A quantitative approach was conducted using descriptive analysis of PressReader usage statistics for online newspapers. PressReader usage statistics were used in the data collection process and analysed using EXCEL to investigate the reading culture and identify the online newspaper most consulted by UNIVEN users from UNIVEN before and during COVID-19.Findings – The study findings revealed that the use of online newspapers decreased during the pandemic and showed that Sunday Times (3047 views), Sowetan (1559 views), Mail and Guardian (1261 views) and Business Day (1131 views) were the most famous newspapers between July 2018 and July 2021 at UNIVEN. This usage was moderate, and libraries must create awareness of the existence of online newspapers and provide a digital infrastructure for users to access and read these newspapers.Implications – The findings of this study can be valuable to media organisations, helping them adapt their content and delivery methods to meet changing needs.Originality – This original study was conducted at UNIVEN in Limpopo province, South Africa.
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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 | 0.001 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".