Governmental policy and regional urbanization of China
Bibliographic record
Abstract
~Phis thesis contains three major parts.The f'irsi:; pe.rt describes G'nina 's urban population grov1th since the founding of the People t s Republic in 19l.J-9.The second par't exs:mines i;he regional variation of urban grm..Jth among the seven regions.Before 19~L9, the f'our regions along the coast Here :much more highly urbanized than.the three regiox1s :tn the inte~r:ior.The situation has cha...~ged greatly in the l)ast quarter century because of' the more l"'apid grovrth of' the c:1.ties in the interior.The third part des.lsN'ii;;h G'Dina 1 s goVG1""11Ya.entalpolicies tvhich have caused this ne1..r t1end of ~0egional urban growth.The most important fact;or a.ff'ect:i.ngthe rapid urban grm..rth of the interio1 ... regions was the economic policy which stressed decentralization and regional self-sufficiency.Unde!' that policy, cities in the interior 1.,egions which were undel"-developed viii i.n tho past have been industrialized rapidly.~l:his has cr-rttSt~Cl tl1e rEtfJid enpans:lon of "l~he ci ti.es in the :tnteri-or.'I~Q.e nation's popu.lationpolicies have also been helping this nev-.1 t1end of regions].u1..,ban development.The birth control program and the rustication program have been particularly effective in the big metropolises along the coast.China's :national security policy, v-rhieh requ.iredeconomic and })Opulation strength along the into:rior border, has also been helping the grm-;th of the cities in the remote areas of' the :tnter:i.Ol".'I'hese governmental policies have had enor:<11o-us effect on the nation's regional urban growth since 191.,.9.ix
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".