Bibliographic record
Abstract
Ebola was a Teachable MomentOn December 29, 2015, when many people were preparing to welcome the New Year of the Gregorian calendar, the World Health Organization (WHO) announced its own cause for celebration: Ebola was gone from Guinea and there had not been a single new case worldwide since November (WHO, 2016a).WHO declared the outbreak had ended in January, after 42 days of zero cases.New isolated cases of Ebola have popped up since then, but everyone agrees the worst is behind us.West Africa's Ebola outbreak was the health news story of 2014 and 2015.Identified in March 2014 and declared an emergency in August 2014, the virus spread quickly in a region that had never before experienced it.The result was harrowing.At least 28,646 people contracted the virus, and 11,323 people died (WHO, 2016c).Even worse is how the outbreak eviscerated the health systems and economies of Guinea, Liberia, and Sierra Leonethree of the world's poorest countrieswhose 22 million people will be affected for decades to come.This Ebola outbreak was a scary drama for people everywhere.I still remember the fear in my compatriots' voices who called in to a Canadian Broadcast Corporation radio programme I was chairing.One man expressed his concern about riding Toronto's subway.A woman revealed her apprehension of associating with African-Canadians.Others predicted doom unless every country immediately closed its borders.Of course these fears were never merited, but the greatest Ebola fear seems to be at the cusp of realization: that we will not learn from this Ebola experience and that we will not address the underlying global institutional failures that allowed it to occur in the first place.What a tragedy it would be if we need a far worse pandemic than Ebola, one that kills many
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".