Introduction to the Supplement: Advancing the practice of operations management and innovation to drive Africa forward in the era of the Fourth Industrial Revolution (4IR)
Bibliographic record
Abstract
The Fourth Industrial Revolution (4IR) is paving the way for new and disruptive approaches to managing operations, innovation, and supply chains. Africa cannot ignore this revolution and afford to stand by while the rest of the world moves forward. In this paper, I invite scholars in the broader field of operations management and innovation to take part in the dialogue, undertake research to advance Africa in the 4IR era, publish their work to ensure that what happens in Africa does not remain in Africa, and inspire or be inspired by others around the world. Consequently, I provide an overview of four papers that were presented at the second African Operations Management Conference in 2019. The conference was hosted by the University of South Africa in partnership with the Africa Automation Fair. These papers focus on the readiness of academic institutions to produce graduates that possess 4IR skill sets, the options available to firms to manage their dependency on suppliers’ supply chain innovation, the design dimensions that impact a supply chain network to perform effectively and operate with resilience while facing disruptions, and the individual components of contractor commitment to incorporate into the decision-making process to deliver road infrastructure projects in a sustainable and socially responsible manner.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.114 | 0.050 |
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".