Survey on Attitude Towards New Management Mode of Linguistic Landscape Programs in Chinese University Language Resources and its Decision Tree Analysis
Bibliographic record
Abstract
In order to survey the attitudes towards the different social participants of intelligent collaboration program on linguistic landscape in Chinese university language resources and university technology resources, find effective methods and applied path to develop new management mode in Chinese university language education and professional training quality of foreign language majors, this article is to carry out attitude survey towards new management mode, namely Government-Industry-University-Research management mode, which can solve the dissatisfied responses for service competence of foreign major students in their real profession field and the implementation difficulty of Standards which issued for the translation revision program on linguistic landscape for local city images construction. Based on the perspective of intelligent diagnosis and intelligent collaboration, this article explores the applied path model on quality monitoring system of linguistic landscape in the aspects of target orientation, content index, implementation process and intelligent collaboration system, which has an important enlightenment and reference role for improving the national quality monitoring system of linguistic landscape by the mix research of qualitative and quantitative methods. And it describes the hierarchical comprehensive evaluation index for the construction of quality monitoring system on linguistic landscape with two steps: 1) the first step is to use analytic hierarchy process (AHP) to decompose the quality monitoring of linguistic landscape into multi-level and multi-dimensional index system, model and quantify the implementation process of quality monitoring, and to construct the first and second-class index system to solve the current problems of quality monitoring and quality improvement; 2) the second step is to use decision tree(DA) analysis to view the evaluation and suggestions of participants in relevant fields on various indicators at all levels from collect feedback results, ensure scientific and reasonable determination of quality monitoring indicators through supplement and scientific revision. The conclusion is that: 1) The new management mode of linguistic landscape programs will benefit the education quality of foreign language majors and improve their service function for the society development. 2) The new management mode will benefit the government department on language technology resources, linguistic technology platform, intelligent collaboration and talents mapping of local city development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".