Bibliographic record
Abstract
Canadian media guru Marshall McLuhan predicted the rise of the “global village” back in 1962. Time and space, he said, would cease to be barriers to communication, enabling people to form relationships on a worldwide basis. In the past 10 years, rapid growth in communication opportunities has validated much of his analysis. All the same, the world has not turned into one great village. Whole regions of our planet have been excluded, as we can see from the map of the world’s Internet connections. The major links bypass the continent of Africa. From the Atlantic Ocean, they touch the Cape of Good Hope before arcing onward to the Pacific, with just the occasional minor branch to the African coast. They look much like the trade routes of the old Dutch and English East India Companies, in fact. A cable running through Africa would be far too vulnerable, even assuming that any local people or businesses could afford fast Internet connections in the first place. So it is that an entire continent can miss out on the communication revolution, causing it in turn to be shunned by the business world. Software firms develop their programs in China and India rather than in Cameroon. A denser network of communications could give people a greater opportunity to participate in the global economy. It might also give them more control over their water supplies or provide them with early signals of global change. Many other problems that humans face are technical in nature, as are the tools we need to confront them. Microelectronics offers tools to better monitor our health. And more flexible, error-aware computers could steer us away from crises. We need tools that are responsive and ubiquitous. We need to measure and control larger areas on a shorter timescale and with much greater accuracy than is currently possible. We still don’t have enough sensors to monitor our climate or imminent earthquakes. We consume too much energy and too many raw materials in our manufacturing plants because we don’t know how to control the processes more accurately.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.740 | 0.545 |
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".