1 Information Sharing During the Klondike
Bibliographic record
Abstract
he freely shared the details and started what would eventually be three waves of rushes. This reflected a social norm of the Klondike, namely that any miner who struck gold would share this information. Miners did not behave this way in other nineteenth-century gold rushes. The article’s hypothesis is that the extreme mining conditions and local geography of the Yukon led to very secure property rights over mining claims. Therefore, it took only a small incentive payment to induce miners to act in the social interest. tarting with the California Gold Rush of 1849 a series of gold rushes occurred along the western side of North America as miners searched for the elusive yellow mineral.1 Eventually these efforts cul-minated in the last, and perhaps greatest, gold rush: the Klondike rush of 1898–1899.2 Located close to the Alaska border, but within the Yukon territory of Canada, the Klondike River is a tributary of the Yukon River. Although called the “Klondike Gold Rush, ” gold was ac-tually found in the smaller creeks that run into the Klondike and Indian Rivers.3 As Figure 1 shows, the entire area was relatively small, and most of the gold was extracted from a half-dozen creeks—Bonanza,
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".