Our world as a learning system: a communities-of-practice approach
Bibliographic record
Abstract
We live in a small world, where a rural Chinese butcher who contracts a new type of deadly flu virus can infect a visiting international traveler, who later infects attendees at a conference in a Hong Kong hotel, who within weeks spread the disease to Vietnam, Singapore, Canada, and Ireland. Fortunately, the virulence of the Severe Acute Respiratory Syndrome (SARS) was matched by the passion and skill of a worldwide community of scientists, healthcare workers, and institutional leaders who stewarded a highly successful campaign to quarantine and treat those who were infected while identifying the causes of the disease and ways to prevent its spread. In such a world, we depend on expert practitioners to connect and collaborate on a global scale to solve problems like this one – and to prevent future ones. Marshall McLuhan's assertion in 1968 that we live in a “global village” has come of age. During the past century, the world has become considerably smaller not only through the effects of the media – McLuhan's focus – but also through science, transportation, the Internet, migration, and the spread of global commerce. At the same time, there has been a proliferation of global problems: environmental degradation, the population explosion, increasing economic disparities between rich and poor nations, threats of biological and nuclear terrorism, disease pandemics, and breakdowns of financial systems. As the world becomes smaller, the problems we face are growing larger in scope and complexity.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".