Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This case details how CIFI Holdings (Group) Co., Ltd. (hereinafter “CIFI”), a private real estate company in China, built up organizational capabilities through different stages of organizational and HR management efforts. It focuses particularly on an organizational change in 2022. At the beginning of the year, Ge Ming, Chief HR Officer of CIFI, proposed an organizational change, but faced initial reluctance from the company’s chairman Lin Zhong and most regional general managers. In spite of this, Ge insisted on a change after analyzing the situation. Eventually, successful trials in two top-performing regions earned Ge approval from Lin. Ge’s proposal addressed two aspects: 1) Personnel structure: In addition to streamlining its organizational structure and reducing the layers of reporting, CIFI should redesign its job architecture and ensure that individuals would go through a competitive hiring process before being appointed. These measures would lead CIFI to downsize while increasing productivity; 2) Compensation: CIFI should implement a role-based broadband pay structure to bring excessively high salaries down to more reasonable levels, thereby reducing overheads. When implementing change, Ge encountered multiple problems but resolved them by adhering to principles, maintaining timely communication, and allowing for some flexibility. Throughout the change process, senior executives such as Lin Zhong and Lin Feng, along with CHRO Ge Ming, each performed their own functions, demonstrating both the philosophy and tactics of change. Through case analysis and discussion, students will understand the concept of organizational capabilities, the ways to build such capabilities, the driving and resisting forces behind organizational change, and the corresponding implementation strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.021 |
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 it