Sounding the alarm: Notes from the Editor-in-Chief Alexandrine Boudreault-Fournier
Bibliographic record
Abstract
I n the fall of 2023, we launched a special call for papers titled "Sounding the alarm" for Anthropologica's newest section, "Seedings," a section dedicated to planting and growing ideas related to current events and debates.Even though we launched our call to "sound the alarm" over a year ago, it is frightening to realize how relevant it is today, perhaps even more so than it was then.Let's look back.Summer 2023 was officially the hottest on record everywhere in the world.In Canada, the 2023 wildfire season was the most destructive remembered, "like no other year, by a stupendous margin" 1 with more than 6,500 wildfires reported by the beginning of September.But Canada was not the only country with these terrifying figures.Unparalleled wildfires in the northern hemisphere destroyed millions of acres of boreal forests, including in Russia, Greece, Portugal and Maui, Hawaii.As we write these lines, thousands of firefighters are still battling the flames in densely populated Los Angeles County.Wildfires are now anticipated calamitous events that the government, people, and survivors must, sooner or later, prepare to fight.Yet, wildfires are striking evidence-a clear alarm bell-that we are losing ground in this quickly and dramatically changing world.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".