Bibliographic record
Abstract
Development has been happening the last two decades and specially the beginning of the 21st century shows that the emerging economies are going to change the formula of the current market dominated leadership. Certainly, many emerging economies as the BRICS countries is a must watch and explore markets from the perspective of being economies that having the strong diversified mix to make more sustainable as the developed countries markets and even more. The work of Malerba et al. (2017) team is highly important since it reflects not only the literature review but also the actual observations of the history of development of the BRICS countries, which resembled by China, India and Brazil. In fact, the book also shows the best practices in how did these new market leaders emerge and become key players in their respective industries. This review is considered of importance since it shows a model for other countries and how to manage the high industries risk and still manage to create market development. The review shows that there are similar industries as automotive, pharmaceutical and ICT industries which can contribute to the success of the developing countries and enable them to become market leaders too. The researchers were very focused on defining market leadership from the following three angles mainly: domination of local market, global reach and the innovative capabilities and capacity of the production or the processes.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".