Bibliographic record
Abstract
An inclusive list of media effects opens many unexpected avenues of awareness and investigation Start with five lists from recent headlines (in no particular order): March 2014-the governments of the United States and Russia engage in a tête-à-tête over Crimea that revolves, largely, around lists.An executive order from US President Barack Obama 'black lists' eleven officials of the Russian government as well as 'any individual or entity that operates in the Russian arms industry, and any designated individual or entity that acts on behalf of, or that provides material or other support to, any senior Russian government official.'3 In response, Russia releases a list of Americans no longer welcome for business, diplomatic, or leisure purposes.Neither list proves effective in addressing the immediate issue of Russia's annexation of Crimea, but both are economical nuggets of information easily digested by the 24-hour news cycle.April 2014-changes to the Canadian Navigable Waters Protection Act (NWPA), proposed by Stephen Harper's Conservative government in its 2012 omnibus budget bill C-45, take effect.The NWPA-an Act in which the default status for Canadian waterways was environmental protection under common law 4 -becomes the Navigation Protection Act.Waterway protection is reconfigured under the new act around economic interests and enforced by a new 'List of Scheduled Waters'.This list denies protection to 99.7 per cent of Canada's lakes and 99.9 per cent of its rivers.Notable exclusions are the Kitimat and Upper Fraser Rivers, which lay along the path of the proposed Northern Gateway Pipeline.Notable inclusions for protection are cottage country lakes in British Columbia and Ontario, where 'powerboat owners will maintain unfettered navigation protections.' 5 Protection is now exception; exception is granted by the 'List of Scheduled Waters'.November 2015-While campaigning for the 2016 Republican Presidential nomination, Donald Trump replies 'Oh, I would certainly implement that-absolutely,' when asked if the United States should create a database of Muslims in the country.He adds that he would employ 'good management procedures' to get Muslims entered into the database.6
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.652 | 0.485 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".