Bibliographic record
Abstract
Between 1978 and 2018 the percentage of the Chinese workforce in the service sector rose from 12.2 to 46.3. A large share of this workforce is in sales, selling products ranging from household goods, insurance, advertising space, and education, to various other services. The proliferation of salespeople in China is facilitated by the dramatic increase in the number of university graduates. Personnel in sales jobs, which are particularly popular among graduates from rural backgrounds with degrees from universities with indifferent reputations, experience an extraordinarily high level of mobility. They typically change jobs every few months, either because they are fired or they pursue better opportunities. Based on one year of fieldwork undertaken between 2015 and 2017, this article shows how the rapid expansion of China's higher education subjects students from rural backgrounds to new inequalities, which, in turn, reconfigure the rural-urban divide into multiple intersecting hierarchies. Building on the concept of complexed development, this article analyzes how salespeople experience contradictory mobilities in a web of intersecting hierarchies. It shows how they achieve upward status mobility by breaking away from agricultural and manual labour and becoming university graduates and white-collar workers; but also, how they sometimes experience downward mobility in terms of income in comparison to previous generations of migrants and their less-educated peers.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".