Bibliographic record
Abstract
As I write this guest editorial, it is the two-year anniversary of the death of Breonna Taylor, an emergency room technician who was shot eight times by police inside her Louisville, Kentucky home when police forced entry during an investigation of her boyfriend, Kenneth Walker.Just over two months later, on May 25, 2020, George Floyd Jr.'s death at the hands of police in Minneapolis, Minnesota, sparked protests against police brutality and anti-Black racism that exploded on a global scale arguably never before seen.But Ms. Taylor and Mr. Floyd were just two in a lineage of deaths of African Americans at the hands of police.So, why the rapidly spreading global enough is enough outcry after Mr. Floyd's death?Dr. Ernest Grant, president of the American Nurses Association believes we may have both COVID-19 and modern technology to thank for that.In a private conversation with Canadian Nurses Association (CNA) leaders about anti-Black racism in 2020, he observed first that technology allowed an unedited and immediate public viewing of a particularly grisly death.The world watched a live streaming as a white police officer pressed his knee on Mr. Floyd's neck for at least eight minutesincluding more than a minute after Mr. Floyd lost consciousness.Mr. Floyd's plea, "I can't breathe," went unheeded and has become a mantra for a new global civil rights movement.But, as Dr. Grant noted, this was hardly the first public airing of the troubling death of an African American person at the hands of police.The difference this time, he suggested, was that because so much of the world was in the first wave of COVID-19 lockdown, people simply were home to notice it.And when they had time to take in the enormity of what they were seeing, millions were propelled to rise up and speak up.Canada was no exception, and Black Lives Matter protests, which I believe many Canadians considered an American movement, arose here too.In an unprecedented day of action, on June 5, 2020, tens of thousands of Canadians of all ages and ethnic backgrounds from coast to coast to coast, came out to protest anti-Black racism in tiny towns and in our largest cities. Prime Minister Justin Trudeau, who took part in the rally on Parliament Hill in Ottawa, observed then that, "Over the past weeks, we've seen a large number of Canadians suddenly awaken to the fact that the discrimination that is a lived reality for far too
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.038 | 0.039 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".