In Shortage: Understanding Global Antibiotic Supply Chains Through Pharmaceutical Trade Fairs
Bibliographic record
Abstract
Many countries have reported supply shortages of antibiotics in recent years. The COVID-19 pandemic has sparked interesting discussions among government officials, public health practitioners, and scholars on how to maintain the security of the global pharmaceutical supply chain and how to decrease dependency on countries such as China. This article discusses the experiences and initial findings of tracing global pharmaceutical supply chains through pharmaceutical trade fairs and events at multiple locations. I argue that the reasons behind antibiotic supply shortages are multifold. Although the geographical concentration of the production of key raw materials and Active Pharmaceutical Ingredients (APIs) in China is considered the main reason for the unstable and disrupted supply chain, it is also important to recognize that the same forces that have driven Western pharmaceutical companies to shift some of their less profitable manufacturing lines to China are also challenging Chinese pharmaceutical companies to adjust and reorient their strategies. As such, antibiotics are facing a conundrum in which the governance of excessive use is complicated by a shortage problem that hampers access to essential drugs.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".