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Record W1982557808 · doi:10.12927/hcpol.2012.23024

Breaking News in Health Policy and the Power of Social Media

2012· editorial· en· W1982557808 on OpenAlexvenueno aff
Jennifer Zelmer

Bibliographic record

VenueHealthcare policy · 2012
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtLawPolitical sciencePresidential electionPoliticsSocial mediaPresidential systemMandate

Abstract

fetched live from OpenAlex

n June 28th, the Supreme Court of the united States announced its decision on the Patient Protection and Affordable Care Act.for the country' s health policy watchers, the wait had been as long -and the tension was as high -as for kids on Christmas morning.The lead-up began months before.There was endless speculation about what the result would be.Would the Court find that the individual mandate was valid or not?What about the other provisions of the Act?How would the vote split?Commentators from across the political spectrum weighed in, as did the stock market and gamblers (Hancock 2012).And finally, the morning of the decision' s announcement arrived.Because it would have profound effects for healthcare, and possibly for the upcoming presidential race, major media outlets treated the story as breaking news.They were broadcasting developments live, each aiming to be first to get the news out.Almost as interesting as the decision itself (Supreme Court of the united States 2012) was the drama that unfolded around the announcement (Goldstein 2012).Within the Court, the usual protocols and ceremonies were observed.The marshal of the Court gaveled the Courtroom to order.The Justices took the bench.Outside, millions were anxiously waiting to learn what the decision would be.With the high volume of traffic, the Supreme Court' s website failed.As a result, everyone outside the Court, including President Obama, were dependent on the media at the Court to get the results.I happened to be in New York for a health policy meeting that day.As the clock ticked past 10 o' clock, participants from the united States were risking repetitive strain injury by pressing the "refresh" buttons on their browsers as fast as they could to see the news as soon as it was available.Bloomberg was first to publish that the decision had been issued and also first to report on the results (Goldstein 2012).fifty-two seconds after the Chief Justice began speaking at 10:06:40, Bloomberg announced that the Court had upheld the Act.Shortly thereafter, so did Reuters, Associated Press and dow Jones.But initial on-air reports and social media posts from CNN and fox News got the results wrong.In just a few minutes, these messages reached thousands, maybe millions, of people.Those without access to a copy of the judgment were left wondering who was right.Ironically, while new technology compressed the news cycle and helped to spread incorrect information quickly, it also contributed to solving the problem.many, including the president' s

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.340
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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