Internationalization Disrupted: Advancing STEM Training in Africa Beyond COVID-19
Bibliographic record
Abstract
The evolving COVID-19 pandemic has made multidimensional impacts on tertiary education, research, and skills development. Yet, no area of academic activity has been more disrupted than the international dimension. These effects are even more pronounced in Africa considering pre-existing challenges, dependence on Northern expertise and resource asymmetries. The scale of the pandemic demands global action, further highlighting the importance of internationalization and “third mission” activities to advance training, research, and innovation to address major STEM challenges. This chapter focuses on the internationalization experiences of the Skills for Employability program, a five-year (2016–2021), $8.5 million project supported by Global Affairs Canada and Mastercard Foundation to advance STEM education and skills development across Francophone Africa. By using the SFE program as a case study, the authors apply an international and comparative lens to critically examine the programmatic shifts which have taken place since the closure of in-person academic activities and suspension of international travel. A key element of this inquiry will be to unpack the institutional shifts which took place as a result of the network-wide lockdown, specific responses and strategies employed, as well as adaptive internationalization measures taken in response to COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".