Gérer dans la tourmente: le management des entreprises formelles au Congo-Brazzaville
Bibliographic record
Abstract
The characteristics of African management are often given as an example to highlight the cultural relativism of management practices and the need to adapt these to the specificities of local context. However, these practices do not depend solely on culture. They are also embedded in a sociopolitical context that may be unstable and sometimes violent. Few studies have attempted to show the role of armed conflicts plaguing many countries - particularly in sub-Saharan Africa - on the management of organizations established in these at-risk zones. An empirical study made with about thirty managers in Congolese firms during the time of the civil war that ravaged Congo-Brazaville shows that consequences of this hostile environment on management practices. It appears that African management, as a whole, tends to be well adapted to situations of crisis and chaos. In particular, the community solidarity that characterizes African firms acts as a social buffer to absorb shocks related to conflicts, and it helps compensate in part for the decline of public institutions. The results of this study also show that despite reinforcement of certain traditional characteristics, in particular autocratic paternalism, Congolese management is rather open to more modern and participative management practices. [PUBLICATION ABSTRACT]
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".