<i>Bayspeak</i>: Narrating China’s Greater Bay Area
Bibliographic record
Abstract
China’s Greater Bay Area (GBA) initiative is the latest and most ambitious attempt to “regionalise” the development process in the Pearl River Delta, promising to accelerate political-economic integration via an innovation-intensive model of growth. Drawing on the techniques of critical discourse analysis, this article presents a deconstruction of the GBA’s emergent spatial imaginary – “bayspeak” – and the rescaled mode of governance that it portends. By way of an interrogation of texts and contexts relating to the GBA initiative, it is suggested that the plan should be taken seriously, if not literally, in its projection of an encompassing and assimilative, if somewhat intransitive, mode of governance. An effort to constitute a mega-region “for itself,” rather than simply “in itself,” the GBA programme has opened a new space (and scale) for co-ordinated development and growth-coalition building under the auspices of the decentralised party-state. As an emergent discourse, bayspeak can be read as hyperbolic, aspirational and symbolic, but as the benign and developmentalist face of the Communist Party line in this economically important but politically stressed region, it may yet prove to be significant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".