Bibliographic record
Abstract
在乌卡（VUCA）时代，风险层出不穷，不确定性增强。如何重塑社会以抵御不可避免的严重冲击成为整个世界都需面对和解决的关键问题。同样，构建适应调整并能快速恢复的韧性对企业发展至关重要。 案例A、B完整描述了房地产“优等生”旭辉地产处理2022年9月突然出现的流动性风险的过程，聚焦韧性领导力和组织韧性，了解在企业发展，尤其是应对危机时的重要作用、韧性领导力的提升以及组织韧性的建构。 案例A的时间跨度为旭辉成立到2022年10月流动性风险真正出现。本案例主要包括三部分内容：一是介绍了旭辉的“软”实力，包括创始人及高管团队的创业经历、管理风格，旭辉的“行者文化”及形成过程。二是旭辉的“硬”实力，即旭辉目前所拥有的、在过去20余年发展中所积累的资源、打造的能力，包括战略规划、组织结构与人才队伍、资金实力等。三是旭辉本次遭遇的流动性风险，风险的起因以及可能的解决方案。学员需代入林中视角，综合考虑旭辉的软硬实力，选择解决方案。分析案例A可以使学员理解韧性领导力及组织韧性的表现及作用。 案例B的时间跨度为2022年11月旭辉公开承认出现流动性风险至2023年7月。案例B呈现了旭辉在做出应对危机的决策后所采取的“三步走”的策略：“蹲下来”揭示了旭辉的选择；“活下去”是旭辉应对风险的应急措施，是旭辉对危机本身的处理；而“站起来”则是旭辉对风险后期及风险过后的规划和蓄力。这些内容有助于学员更加深入地掌握组织韧性的形成。 案例A、B详细描述了旭辉面对流动性风险危机时的选择和行动。分析该案例有助于学员理解韧性领导力、组织韧性在企业面对重大决策时如何发挥作用，并掌握提升韧性领导力和组织韧性的方法。
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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.096 | 0.025 |
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".