Bibliographic record
Abstract
I n the National Post in 2008, columnist John Moore related details of a murder: "a man fatally shot his wife in the chest and got away with it."Our reaction is an immediate sense of outrage at the ills of modern society.This is yet another example of wanton domestic violence and of a judicial system that has failed, once again, to bring the perpetrator of a horrifying crime to task -"bleeding heart liberal judges and their hugs for thugs."We later learn that the accused was an elderly man diagnosed with a terminal illness, married for many years to a woman who had developed Alzheimer's disease.He was fearful she would suffer unduly without his care.Knowing, too, that his own death was imminent, he chose to end her life.He was never charged with the murder and was released home to await his own death, at least content in the knowledge that his wife would not endure prolonged neglect and suffering.The context, says Moore, removes our outrage; we now understand both the husband's and the judge's decisions (Moore 2008, April 7).After learning this, we might then wonder, on reflection, "How could I have been so stupid to have made the first judgment?"We make decisions continuously throughout our waking lives.These decisions vary in complexity from the simple, automatic processes involved in executing a well-rehearsed habit, such as driving a car, through more consequential decisions, such as choosing a partner or deciding on a career.Decisionmaking is the most important thing we do; it is the engine of all human behaviour.In fact, how we live our lives derives from how we make decisions (Gigerenzer et al. 2002).Yet, it is only recently that a consensus has emerged about how the overall process of decision-making occurs; this consensus appears to explain the different ways in which we make decisions, as well Context Is Everything or How Could I Have Been That Stupid?Pat Croskerry Understanding Decision-Making in Healthcare and the Law
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".