Bibliographic record
Abstract
BRICS has emerged as a promising club of emerging economies with a substantial contribution to the global trade and economy (Iqbal et al., 2015). According to the latest statistics, the total Gross Domestic Product (GDP) of BRICS countries in 2019 was US$ 21,154,265 million which is 53% of the total GDP of G7 countries (Canada, France, Germany, Italy, Japan, UK, USA) and it is expected to go up in the coming years (UNCTAD, 2021). The annual average of the GDP growth for BRICS in 2019 was 2.78% (Brazil 1%, China 6.1%, India 5.3%, Russia 1.3%, South Africa 0.19%) while for the G7 countries it was 1.17% (UNCTAD, 2021a). The trade balance expressed in terms of GDP for BRICS was 2.2% in 2019 while for the G7 countries it was (-) 0.42%, again suggesting a significant contribution of BRICS in the International Trade (UNCTAD, 2021b). This shows the potential of BRICS to outpace G7 countries in certain indicators. There can be no better time for publishing on BRICS studies when a Covid19 affected world is looking towards BRICS for more than one reason. BRICS cooperation in a multitude of domains (Luckhurst, 2013), from healthcare to the digital payments system, has made it a hot cake in multilateral cooperation studies (Zhongxiu and Qingxin, 2020). There can be no denial about the potential economic might of BRICS economies, and as studies suggest it will continue to bolster (Nayyar, 2020). Published literature suggests the case for BRICS as an emerging block (Iqbal and Rahman, 2016; Iqbal and de Araujo, 2015), though there are studies questioning the existence of a block (Beausang, 2012). The absence of a ratified agreement does not indicate the absence of a block. The cooperation among BRICS is proving to serve the purpose of a block without creating a complex legal structure. Cooperation in the emerging areas among BRICS countries is also taking its own pace such as Trade (Rahman, 2016; Rahman et al., 2020), Climate Change (Rahman and Turay, 2018), Sustainable Development Goals, Smart Cities, Industrialization 4.0 et cetera. Considering the increasing significance of BRICS countries in the Global Economy and its competitiveness (Loo and Iqbal, 2019), there was a need to deliberate on BRICS as an emerging block. The special issue in hand is the outcome of such deliberations, having a variety of articles focusing on different dimensions of BRICS issues and cooperation.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.057 | 0.034 |
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".