Bibliographic record
Abstract
Located in linguistically and culturally diverse regions, China’s border hospitals are strategically positioned as a key hub linking China’s healthcare communication to the neighboring countries. Language constitutes a key mechanism for articulating the geo-semiotic discourse of China’s regional integration in medical settings. Drawing on the concept of semiotic landscape (Gorter, 2018; Jaworski & Thurlow, 2010), this study examines how different linguistic and multimodal resources are strategically deployed to construct an image of China’s border hospital. This study was conducted at a China’s county hospital bordering Laos and Vietnam in April 2024 and data were collected from hospital public signage, online reports and other artefacts, semi-structured interviews with a full-time translator, a vice president and a office manager at the hospital. Findings center on the distribution and functions of different languages, namely Mandarin, English, Lao, Vietnamese and ethnic minority languages. A particular focus is given to the semiotic appropriation of how different languages work with other modes to construct the meaning-making process for local-transnational-global nexus of healthcare production and consumption. This study illustrates how language and semiotic resources can become the totality of material constituents for reconfiguring the multiple identities of China’s multilingual healthcare communication at periphery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".