The Antimicrobial Resistance Crisis: How Neoliberalism Helps Microbes Dodge Our Drugs
Bibliographic record
Abstract
The urgent public health threat of antimicrobial resistance (AMR) has received much attention from the world's most important health agencies and national governmental organizations. However, despite large investments being allocated to strategizing national and international plans for addressing this public health problem, the incidence of untreatable, antimicrobial-resistant diseases continues to rise in many nations. To avoid returning to a society in which common infections once again become deadly, one must consider the often-ignored root causes driving inappropriate behaviors relating to antimicrobial use, such as the history of antimicrobial drug development, the effects of commodifying health-related services, and the rise in social inequalities. By employing the lens of political economy to analyze the phenomenon of AMR on national and international scales, it is found that the acceptance of neoliberalism as a governing ideology by authorities is hindering our ability to globally combat AMR through the depoliticization of issues that require political intervention to stimulate change. Differences in level of AMR and approaches to pharmaceutical governance between social democratic and liberal welfare states provide validity to this hypothesis.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".