Representation of Pragmatism in Scholarly Publications on COVID-19
Bibliographic record
Abstract
Pragmatism is an important resource that has helped higher education institutions (HEIs) in Lesotho and South Africa to complete the 2020 academic year even when they were affected by COVID-19. Pragmatism is a philosophy of human actions combined with experiences in order to produce outcomes or consequences, where the reality is about what works according to individual needs based on a specific situation. During the COVID-19 era, pragmatism has been represented by the use of learning management systems (LMSs) and social media sites (SMSs). The representation of pragmatism, based on ten sampled publications of this study was divided into performance- (driven by LMSs) and competence-based (driven by SMSs) curricula. The purpose and objective of this study was to explore and understand the representation of pragmatism in ten scholarly publications purposively sampled for this study on education during the COVID-19 era. Document analysis framed by pragmatic paradigm, critical discourse analysis (CDA), and community of inquiry (CoI), was used to generate data for this study. The findings concluded that pragmatism was the reason for HEIs saving the 2020 academic year: pragmatism harmonised the tension between LMSs and SMSs which existed even before the COVID-19 era. Consequently, this study recommends the application of pragmatism in any uncertainty/novelty situation in education, in order to address individual needs before professional and societal needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".