Bibliographic record
Abstract
Abstract Let us start with a map. Unfold like a painted fan a mercator projection, a view from high above the earth encompassing the Pacific Ocean, with Asia on the left and the Americas on the right. The arc of the Pacific Rim sweeps from the blotch of Australia to Indonesia and Southeast Asia, up the coast of China past the Korean peninsula and Japan, around Alaska and down the West Coast of Canada and the United States, tailing off to the tip of South America. Imagine the map as a parchment through which to relive the past, a chart to trace the stories of people as they move about, leaving a trail of dotted lines that follow them from place to place. The story of these people is one of movement, and like a travel-worn atlas that shows the scrawled markings of roads taken and places seen, this map will show journeys and tell stories of how people came to see things previously unseen, how they tried to understand what they saw, and how they often kept going somewhere farther in order to understand what they had just seen. Place-names coalesce on this imaginary map, given meaning within and connected to the lives of our travelers. Guangdong Province in southern China, Japan, Hawaii, Seattle, San Francisco, Stockton, Los Angeles, Butte, Tule Lake, Iowa City, Nashville, and, on the extreme edge of our map, Chicago.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.492 | 0.248 |
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".