Bibliographic record
Abstract
Ever wondered what it would be like to be a street magician in Paris? A fish farmer in Norway? A costume designer in Bollywood? This playful and accessible look at different types of work around the world delivers a wealth of information and advice about a wide array of jobs and professions. The value of this book is twofold: For young people or middle-aged people who are undecided about their career paths and feel constrained in their choices, A World of Work offers an expansive vision. For ethnographers, this book offers an excellent example of using the practical details of everyday life to shed light on larger structural issues.Each chapter in this collection of ethnographic fiction could be considered a job manual. Yet not any typical job manual—to do justice to the ways details about jobs are conveyed in culturally specific ways, the authors adopt a range of voices and perspectives. One chapter is written as though it was a letter from an older sister counseling her brother on how to be a doctor in Malawi. Another is framed as a eulogy for a well-loved village magistrate in Papua New Guinea who may have been killed by sorcery.Beneath the novelty of the examples are some serious messages that Ilana Gershon highlights in her introduction. These ethnographies reveal the connection between work and culture, the impact of societal values on the conditions of employment. Readers will be surprised at how much they can learn about an entire culture by being given the chance to understand just one occupation. Contributors: Lovleen Bains, Mumbai; Chiwoza Bandawe, University of Malawi; Joshua A. Bell, Smithsonian Institution; Michelle Bigenho, Colgate University; Warren Chamberlain, Vita Needle Company, Massachusetts; Melissa Demian, Australian National University; Ilana Gershon, Indiana University; Kathryn Graber, Indiana University; Graham M. Jones, MIT; Amanda Kemble, University of Michigan; Briel Kobak, University of Chicago; Corinna Kruse, Linköping University, Sweden; Joel Kuipers, The George Washington University; Carrie Lane, California State University, Fullerton; Jean Lave, University of California, Berkeley; John Law, Open University; Heather Levi, Temple University; Marianne Elisabeth Lien, University of Oslo; Caitrin Lynch, Olin College; Loïc Marquet, Paris; Winnifred Fallers Sullivan, Indiana University; Chris Swift, Leeds Teaching Hospitals; Claire Wendland, University of Wisconsin–Madison; Clare Wilkinson-Weber, Washington State University Vancouver; Helena Wulff, Stockholm University
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".