Bibliographic record
Abstract
An outbreak of professional and popular articles followed upon the West African Ebola epidemic which began December 2013 in Meliandou, an isolated village in Guinea. If they teach nothing else, it is that a complex of events combine to permit a local infectious outbreak to assume regional if not, in the end, pandemic status. These contributions may be separated into several distinct categories. The first includes a host of reports describing Ebola’s virology and its history. They range from clinical reviews of what has been learned about the virus since its discovery in 1976, 1 to focused reports on treatment issues in the field. 2 A second set has focused on the ‘zoonotic niche’, the biogeographical environment that promoted Ebola in local reservoirs, especially those including bat species populations, 3 as a source of both the most recent and perhaps previous outbreaks as well. 4,,5 A third category has added socioeconomic and geopolitical factors to that biographical field. Popularly, this literature considered the presumed failures of the World Health Organization 6,,7 and the budget cuts that had decimated its staff. 8 But that was only part of a history of more general failure in the creation of regional and global programmes of health and development. 9 To that must be added a local and regional anthropology documenting the local distrust of official intervention by regional officials who seemed, at first, to first ignore and then blame affected populations. 10 All these elements—anthropological, biogeographical, economic, political and social—contributed to what became the most deadly and dangerous Ebola epidemic in history.
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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.032 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".