Looking Back and Moving Forwards.
Bibliographic record
Abstract
… the final test of a leader is to leave behind the conviction and will to carry on …" -Walter Lippmann January always gives one pause for reflection on events of the year past -the good and the bad, the successes and failures, the gains and losses.On the world stage, we have witnessed many notable events in 2016 -national uprisings, warring factions, thousands seeking refuge, mass shootings and terror attacks, precarious economies, and divisive referendums and elections.Closer to home, in Canada and in the realm of healthcare, debate and deliberation continues about assisted suicide, the legalization of marijuana, safe injection centres, escalating suicide rates among aboriginal youth and a new health accord.Despite the unfolding of many controversial issues, ours remains a largely a peaceful nation and among the most fortunate in the world.The year was also marked by the deaths of many renowned public figures: Castro, Prince, Bowie, Ali, Princess Leia, Howe and Toffler are among the dozens of people who have left us with indelible yet diverse traces of notoriety.Canadian nursing has lost significant, long-time contributors to the profession over the past year too, including Harriet "
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.018 | 0.018 |
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".