Collecting data on a global scale: from local to international and back again
Bibliographic record
Abstract
In the atomic world ushered in by the detonation of the first atomic bombs over Japan in 1945, radioactive strontium (or strontium-90), a by-product of nuclear explosions, soon emerged as a substance of popular, political, and strategic concern. This article considers three data collection programs that established strontium-90 as a global problem while representing different strategies to construct the global dimension — a secret data collection program, code-named Project Sunshine, conducted by the US Atomic Energy Commission taking advantage of the US diplomatic and military networks around the globe; a data collection project organized by the UN at an inter-governmental level; and the Baby Tooth Survey, mounted by a group of concerned citizens and public-minded scientists in St. Louis, Missouri, to reconstruct the effects of the world-wide weapons-testing activity on the local children. All three data collection programs were traversed by cold war tensions but ultimately helped to turn the tide on nuclear weapons testing and led to the signing of the first partial atomic test ban treaty in 1963. While the single projects have already received historical attention, viewed together they invite us to rethink the categories of the «local» and the «global» as articulated around the problem of global fallout.
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".