Feature Story: Reflections on 40 years of journalism
Bibliographic record
Abstract
Sitting alone inside a satellite truck on a chilly December 2012 afternoon in Newtown, Connecticut, I noticed something unusual happening. Tears were rolling down my cheeks. A couple of days earlier, I had flown from Toronto to join an 11-person Global News crew in the small New England community where twenty children and six teachers at the Sandy Hook Elementary School had been gunned down by a troubled young man who killed his mother before turning the gun on himself. As I was watching my cameraperson's video of a community coming together to mourn the loss strangers placing flowers and mementos on a makeshift memorial; locals paying respects at a funeral home suddenly, I felt overwhelmed by the enormity of what had happened.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 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".