Bibliographic record
Abstract
Global measurement of entrepreneurial activity shows that entrepreneurship in Africa is growing. Similarly, research on African entrepreneurs and their entrepreneurial behaviour appear in an increasing number of scholarly articles. However we note an obvious neglect of a context sensitive approach to both the measurement of entrepreneurial activity and researching entrepreneurship in Africa. In this theoretical paper, we use postcolonial theory, and more specifically Edward Said’s idea about the misrepresentation of the Orient by the Occident, to illustrate how existing global measures of entrepreneurial activity fail to provide a real account of entrepreneurship for Africa. We then propose postcolonial theory as a useful analytical tool for researching Africa’s case. To justify this proposal, we analyse the region’s colonial history, large informal sector, heterogeneous population of entrepreneurs, social entrepreneurship and current geopolitical changes. We then use Homi Bhabha’s concept of the ‘third space’ and Gayatri Chakravorty Spivak’s concept of subalternity to critically analyse entrepreneurship research in Africa. To end, we propose a shift towards methodologies which are more context sensitive, recognise the postcolonial setting of Africa and allow agency to emerge during fieldwork.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".