Bibliographic record
Abstract
Looking Back, Looking Ahead T en years on, Y2k seems a distant memory.This year, computer glitches were far from the minds of most revellers as they marked the beginning of 2010 with the blast and blaze of fireworks.Instead, 2009 was the year of H1N1.It led health news coverage in the global media (madison 2009; Branswell 2010) and was also one of the most common topics of general conversation, as reflected in posts to social networking sites (Backstrom 2009; @Abdur 2009).In most countries, Google search volumes for H1N1 peaked in the early stages of the outbreak.1 Canada, though, saw a second spike in October/November.I was not surprised to see the data.from chats with taxi drivers to exchanges with health experts, conversations during a trip to Toronto last fall all seemed to turn to H1N1.The balance was noticeably different at home in denmark, in London where I was the week before and in Colombia where I travelled the following week.Ironically, the same trip also brought to my attention differences in public communications strategies regarding the pandemic.Before I left, I scoured a large number of government travel advisories.At the time, both Australia and the united kingdom started their advice for travellers to Colombia with bold-font announcements about H1N1.They then went on to highlight the risk of violent attacks, kidnapping and other crime.Canada, the united states and denmark reversed this order or did not mention H1N1 at all in their country-specific travel advice.(In the end, I received a warm welcome and my trip was entirely trouble-free.)In this and much more serious ways, H1N1 tested existing communications protocols; stretched the boundaries of what we know about influenza, pandemics and how best to prepare and respond; and led to many new questions.While experience and the evidence base are growing, challenges existed at all levels, from GPs who cared for worried patients to the corridors of the World Health Organization' s headquarters in Geneva.The differences that I saw in my travels piqued my interest in how various jurisdictions were managing their H1N1 response.my colleagues and I at the International Health Terminology standards development Organisation in Copenhagen conducted an informal scan of vaccination policies and programs in mid-November 2009.We looked at government websites from European countries to identify when H1N1 immunization began, how vaccination was being done (e.g., through physician offices
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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 | 0.064 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.031 | 0.053 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".