Digital Agricultural Space Construction and Practice in the Context of Rural Revitalization: A Case of the Tea Industry in Zijin County, Guangdong Province
Bibliographic record
Abstract
With the promotion of China's rural revitalization strategy, rural industrial formation based on digital technology is increasingly emerging. How digital technology stimulates rural industrial development as a new infrastructure force and guides the transformation and reconstruction of rural space has become a topic of concern for the Chinese government. Using field research and semi-structured interviews, this research took the tea industry in Zijin County, Guangdong Province, as an example to explore the digital construction process of rural agricultural space. Furthermore, it focused on how digital technology promoted the social and spatial organization transformation of rural areas and analyzed the operation mechanism of digital agricultural space. The main findings of this study are as follows: (1) The introduction of digital agricultural technology realizes real-time monitoring of the production space, which helps break the "black box" dilemma arising from the physical isolation of the production and sales sides, and promotes the construction of a logic for agricultural modernization operations. To support the routine operation of the technology platform, digital infrastructure and the introduction of skilled human resources stimulated the creation of new rural spatial functions. 2) Differences in the digital practices of different rural entities were observed. First, targeted digital agricultural space construction leads to differences in resource allocation among rural enterprises of different scales, which intensifies the differential development of rural space construction. Second, the top-down-led digital construction of rural areas has differences between the implementation strategies of governance subjects and the actual needs of local enterprises. This is mainly reflected in the lack of coupling between the integration of digital infrastructure resources and the granting of hierarchical technical knowledge. In addition, grassroots farmers form cognitive inertia to traditional production models and have insufficient knowledge of digital technologies, making it difficult for them to participate in the everyday construction of digital rural discourse systems. 3) Digital technology is leading the rurality turn, i.e., features digital intervention in the construction of agricultural space. Under the discourse of precise poverty alleviation and rural revitalization, the logic of digital rural operation in Zijin County centers on the three-subject framework of government, enterprise, and villagers. With the intervention of digital technology, a hybrid of multiple subjects, networks, and meanings guides the structural transformation of rurality. Overall, digital technology has triggered a reconfiguration of the spatiality of the Chinese countryside. On the one hand, it drives the spatial transformation of rural areas by guiding the transformation of rural social and spatial organization. On the other hand, the current top-down digital technology sink model of rural areas needs to be further improved due to the differences in multiple subjects in rural areas. To broaden the effectiveness of digital technology in promoting the development of rural areas, future construction of digital rural areas should deepen the bottom-up participatory transmission path and guide the participation of more diverse rural subjects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".