Bibliographic record
Abstract
Tsiang, Sarah. Warriors and Wailers: One Hundred Ancient Jobs You Might Have Relished or Reviled. Illus. M. Newbigging. Toronto: Annick Press, 2012. Print. I did not confirm that there really are 100 jobs in this colourful and well-illustrated little book but there certainly are many, and a wide variety. The index does list the title’s “wailer” but not “warrior.” In fact, there are several warrior-ish jobs such as watchtower guard, female warrior, military service-conscripted, Shaolin warrior monk, and even a few illegal warrior-like positions (e.g., assassin, pirate admiral, and rebel leader). This book is one in a series of “jobs in history” books by Annick Press but is not merely a formulaic adaptation. These really are ancient Chinese jobs and many probably did not exist elsewhere (e.g., pearl maker, jade worker, lacquer worker, acupuncturist, or bone diviner). It also seems the jobs are arranged in an appropriate order, reflecting decreasing rank and honour from the Emperor and imperial jobs, through scholars and servants, to peasant farmers, then artisans and craftspeople, and finally, merchants who did not grow or make anything (with some illegal jobs at the end). The short introductions to the Chinese dynastic method of counting years, education, rank and honour, religion and schools of thought are really helpful in providing context to a culture that is quite different for most readers. If I could suggest one addition, it would be a consideration of how we know today about jobs that existed between the Han and Tang dynasties (206 BCE to 907 CE) more than 1000 years ago. The answers (presumably a combination of the ancients’ meticulous record keeping and desire for meaningful artistic ornament, combined with conscientious preservation, and modern skills in the humanities and social sciences) would shed further light on interesting jobs, careers, and intellectual pursuits for today’s students. There may be a few, minor short-comings. One is a subtle, underlying theme suggesting more individual choice of careers than there actually was. Another is some job “descriptions” refer to something that maybe happened only once in history – not really an everyday job. Also, the examples of Chinese script, while illustrative, seem to be just a little off in terms of balance or correct stroke order. But these are minor. Overall, this book is a wonderful introduction to Ancient China that should stimulate further exploration into the fascinating study of History. Recommended: 4 out of 4 starsReviewer: David SulzDavid is a Public Services Librarian at University of Alberta and liaison librarian to Economics, Religious Studies, and Social Work. He has university studies in Library Studies, History, Elementary Education, Japanese, and Economics; he formerly taught in schools and museums. His interests include physical activity, music, home improvements, and above all, things Japanese.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.044 |
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".