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。
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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 | 0.082 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.019 | 0.051 |
| Scholarly communication | 0.035 | 0.047 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 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".