Bibliographic record
Abstract
Thomas Cook started it all with his meticulously organized archaeological tours up the Nile. He harnessed the revolutionary technologies of Victorian travel to a growing desire on the part of the middle class to explore the world and its ancient history. Cook was the first to realize the potential of the railroad for group tours. A devout Baptist and an advocate for temperance, he began his business by organizing rail excursions to temperance meetings in nearby towns in central England. The enterprise was so successful that he took advantage of steamships and continental railroads to organize what we now call package tours to France and Germany. From that, it was not much more difficult to organize tours to Egypt and the Holy Land, now readily accessible thanks to the new technology for Victorian travel: the railroad, the steamship, and the telegraph. Then, in the twentieth century, came ocean liners, massive cruise ships, and the Boeing 707, followed by the jumbo jet, all of which together made archaeological travel part of popular culture. We live in a completely accessible world of intricate airline schedules and instant communication, where you can visit the great moiae of Easter Island as easily as you can take a journey to Stonehenge or the Parthenon, the difference being a longer flight and the need for the correct visas and a foreign rental car at the other end. And if you become sick or injured, you can be evacuated from most places within hours: Peter Fleming or Ella Maillart would have been in real trouble had they become sick or injured in the vast expanses of central Asia. We forget that to travel east of the Holy Land was considered highly adventurous until after World War II, and that central Asia was virtually inaccessible to outsiders until the late twentieth century. Much of the adventure of archaeological travel has vanished since the 1960s in a tidal wave of mass tourism and its attendant businesses. Leisure travel is now the world’s largest industry, and the mainstay of many national economies, including that of Egypt, where at last count six mil-lion tourists visit each year. According to Statistics Canada, global cultural tourism will grow at a rate of about 15 percent annually through the year 2010.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.018 |
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".