What Now for the Zimbabwean Student Demonstrator? Online Activism and Its Challenges for University Students in A COVID-19 Lockdown
Bibliographic record
Abstract
University student activism is generally characterized by protests and demonstrations by students who are reacting to social, political, and economic challenges. The COVID-19 pandemic revolutionized university student activism, and closed the geographical space for protests and demonstrations. The pandemic locked students out of the university campus, thus, rendering the traditional strategies of mass protests and demonstrations impossible. The COVID-19-induced lockdowns made it difficult, if not impossible, to mobilise for on-campus demonstrations and protests. It seems the pandemic is the last nail in the coffin of on-campus student protests. This theoretical paper uses a collective behaviour framework to explain the evolution of student activism in Zimbabwe, from the traditional on-campus politics to virtual activism. It discusses the challenges associated with cybernetic activism. The paper argues that, despite challenges, Zimbabwean university student activists need to migrate to a new world of digital technology and online activism. In the migration to online activism, students activists face a plethora of challenges. On top of the already existing obstacles, activists face new operational challenges related to trying to mobilise a constituency that has relocated to cyberspace. Student activists utilize the existing digital infrastructure to advance their politics, in spite of a hostile state security system and harsh economic environment, and other operational challenges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".