Bibliographic record
Abstract
O 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 Breaking News in Health Policy and the Power of Social Media editorial
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 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".