Knowledge map of Latin American research on management: Trends and future advancement
Bibliographic record
Abstract
The objective of this article is to determine the dynamics of the evolution of management as an academic research discipline in Latin America and the Caribbean (LAC) in the past 25 years. The methodology used in the research comprises a combination of co-word analysis with Social Networks Analysis organized in a six-step procedure. First, the data retrieval was carried out; second, a list of key words related to the management discipline was created; third, a key word co-occurrence matrix and its normalization using Salton’s Cosine was done; fourth, each key word was assigned to the research line it represents, taking into consideration the 25 divisions that make up the Academy of Management Society; fifth, the internal cohesion was calculated for each research line using the density of the words network that makes it up and each line’s centrality degree; and, sixth, a strategic diagram was created representing the stage of development of each research line. The results show how the research lines Strategic Management Process and Innovation & Technology Management have formed the backbone of the development of management as an academic discipline in LAC. We also present how research lines that are necessary for the economic and social development of the region such as Entrepreneurship, Cooperative Strategy and Public Sector Management appear as peripheral underdeveloped lines. Finally, we address possible strategies for future development of the management discipline in LAC.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.027 | 0.054 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".