Bibliographic record
Abstract
This case describes the plight of Li Fen, a human resources (HR) manager. In 2008, Li began working as a management trainee at Company A, a smartphone manufacturer. After landing a formal job at the company, she handled HR and administrative tasks for a new project and a big company project that had encountered some difficulties. She handled both projects effectively due to her self-discipline and strong work ethic. Nevertheless, she found it difficult to get along well with her colleagues, including superiors, peers, and subordinates, who frequently complained about her, and they even got into heated arguments. She had never considered that interpersonal relationships could get in the way of her work. Disheartened, she returned to company headquarters to enhance her professional skillset. However, would better professional skills alone help her deal with these interpersonal issues? People often joked that Li had low emotional intelligence (EQ), but she could never quite put her finger on the problem. In the past, she had believed that work was just about getting things done and that managers should not preoccupy themselves with other people's emotions or show empathy to others. Now, however, she began to question this assumption: Could it be that EQ was actually critical in the workplace, and in that case, how could she improve her EQ to forge stronger interpersonal relationships?
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".