Bibliographic record
Abstract
本案例描述了宏村开发旅游业的历程。宏村是一个具有800多年历史的古老村落，因历史原因，村中修建有典型的徽派古建筑和独特的水系,风光秀丽。由于地处皖南山区，交通闭塞，经济相对落后，古建筑和民风民俗保存较为完整，宏村具备发展旅游的基础。 宏村先后被多个主体开发过，包括县旅游局、村办旅游企业等，但一直发展的不成功。从事地产开发的黄怒波因招商引资来到宏村，创立了京黟公司，并聘任以赵总为首的管理团队，对宏村进行旅游开发。开发初期涉及村民、游客、村委会、当地政府等多个利益相关方，近些年又增加了新一代宏村人、外地经营者等新的利益方。 作为开发主体的京黟公司该采用何种模式开发宏村的旅游资源？如何处理与利益相关方之间的关系？宏村旅游创造的价值，应该如何在政府、企业、村民三方之间分配呢？本案例主要围绕这些问题展开。
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 | 0.000 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".