Landscapes of boom and ruination: politics of seeing in China’s “tin capital” Gejiu, 1912-1949
Bibliographic record
Abstract
This article argues that landscapes of ruination and wasted labour are not an unfortunate finale in the history of mineral-rich cities under the “resource curse” but an always ongoing reality from the start of their involvement in the capitalist world market and pursuit of industrial modernity. Focusing on narratives about the “tin capital” Gejiu in southwestern China during the first half of the 20th century, I conduct a historical analysis and examine how geological and sociological experts introduced new politics of seeing and exploiting land and labour. Boasting the landscape of the industrial boom, Chinese intellectuals and technobureaucrats alike considered geological discoveries and resource extraction as a sign of a nation’s civilizational status and modernization achievements. Their fascination with this outlying small town also exemplified a nationalist claim to the new Republican State’s southwestern borderlands in an era of imperialist territorial divisions. Nonetheless, they had to constantly explain (away) appalling sights of environmental ruination and social polarization, often through promises of mechanization or labour welfare. Offering a close reading of travelogues, social surveys and scientific reports, I challenge the conventional narrative from prosperity to decline and reveal landscapes of ruination at the heart of modernization and nationalist discourse of conquering and utilizing natural resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".