Beijing from Below: Stories of Marginal Lives in the Capital’s Centre, by Harriet Evans
Bibliographic record
Abstract
B eijing from Below: Stories of Marginal Lives in the Capital's Center providesa unique look into the fragile livelihoods of what the author Harriet Evans refers to as the "subalterns of history" via profiles of inhabitants of the Dashalar area in Beijing's central district.Using subaltern historical accounts, the author seeks to challenge the dominant narrative of the Chinese communist party's success story.Her findings contradict the widely held belief that urban poverty was practically non-existent during the Maoist era because the government provided some form of assistance to every citizen, resulting in a society where everyone was treated fairly and equally.As per this storyline, urban poverty is a product of economic liberalization, which has resulted in an increased social and economic difference in metropolitan areas.Contrary to this perspective, historical accounts of minority personal experiences like those Evans has documented in her book show this prevailing worldview to be exaggerated.Discrimination against Dashalar people in Beijing demonstrates how the borders of regional and native place identification, as well as their intersection with class, are inextricably linked to deeply established structures and attitudes of inequality and contempt among China's Han population.Evans argues that inhabitants' claims of local identity do not reflect a sentimental attachment to the past, but rather a rejection of exclusion and a yearning for acknowledgement.Evans challenges official narratives of China's socioeconomic development by raising critical issues about the subaltern's role in history by focusing on the experiences of the Dashalar's elderly residents.Evans' research methodology, based on an empirical study undertaken between 2007 and 2014, incorporates document analysis, fieldwork, and historical accounts to create a cohesive whole.A total of seven chapters are
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".