Bibliographic record
Abstract
This book provides a comprehensive examination of the pharmaceutical and medical device industry, including analysis of its current trade and innovation strategies. Opening with a survey of the global pharmaceutical and medical device industry, Bhardwaj outlines the growing trade and trade interdependence among countries in the global supply chain. He adopts a trade competitiveness approach to analyze patterns of product specialization and examines the drug discovery process and its challenges in translating bioscientific knowledge into lifesaving products. Bhardwaj argues that further economic integration, collaborative R&D, and digital technologies may help accelerate productivity and address global challenges of escalating drug costs, neglected tropical diseases (NTDs), and pandemic risks. The book also considers how the industry may further green its supply chain, and thus contribute to SDG Goals 3 (Good Health and Wellbeing) and 12 (Responsible Consumption and Production), before closing on a review of China and India, major players who have the potential to become drivers of low-cost medical products and innovations. With its evidence-based analysis, this book will be of great interest to researchers in pharmaceutical studies, supply chain management, global health, and health economics, as well as policymakers and professionals interested in the global issues facing the industry.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".