Bibliographic record
Abstract
近屿智能是一家以AI视频面试产品为核心的人力资源领域产品和解决方案公司，其创办的初心是希望通过AI技术帮助企业既快又精准地挑选到合适的候选人。然而，在和著名上市公司Y集团的沟通中了解到，Y集团对AI面试产品有诸多疑虑并提出严重质疑，近屿智能创始人方小雷和他的团队遇到了挑战：如何说服Y集团采购其产品？AI面试到底行得通吗？ 案例首先介绍了公司和行业背景，包括创始人方小雷的个人经历及其开发AI面试产品的初衷、AI在HR SaaS行业的应用、近屿智能的主要竞争对手。其次，案例描写近屿智能主要产品“AI得贤招聘官”从研发到市场的三次技术迭代；针对不同行业的客户打磨产品、完善服务流程。最后，案例介绍了近屿智能在Y集团遇到的难题：客户对AI面试产品的疑虑和质疑、竞争对手的搅局等。
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.013 |
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; both teacher heads agree on what is shown here.
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".