Bibliographic record
Abstract
Globalization is a fairly recent addition to the panoply of concepts describing the internationalization of health concerns. What distinguishes it from 'international health' or its newer morphing into 'global health' is a specific analytical concern with how globalization processes, past or present, but particularly since the start of our neoliberal era post-1980, is affecting health outcomes. Globalization processes influence health through multiple social pathways: from health systems and financing reforms to migration flows and internal displacement; via trade and investment treaties, labour market 'flexibilization', and the spread of unhealthy commodities; or through deploying human rights and environment protection treaties, and strengthening health diplomacy efforts, to create more equitable and sustainable global health outcomes. Globalization and Health was a pioneer in its focus on these critical facets of our health, well-being, and, indeed, planetary survival. In this editorial, the journal announces a re-focusing on this primary aim, announcing a number of new topic Sections and an expanded editorial capacity to ensure that submissions are 'on target' and processed rapidly, and that the journal continues to be on the leading edge of some of the most contentious and difficult health challenges confronting us.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".