ETS 2025 技能分类法
Bibliographic record
Abstract
在一个由快速的技术进步推动发展的时代，技能的重要性愈加凸显。为了迎接现代就业 市场的挑战，许多员工试图重塑与提升自己的技能。这尤其需要他们理解雇主们看重哪 些技能以及通过哪些途径可以获取这些技能。本文回顾了过往有影响力的劳动力必需技 能框架，并提出了 ETS 2025 技能分类法。这一分类法内容广泛，涵盖认知、人际关系、 内省、数字信息和终身学习技能，并说明了各类技能的定义与进行技能评估时需要考虑 的因素。需要指出的是，ETS 2025 技能分类法尤其强调了科学技能、远程工作和可塑性 等新兴技能，因为不断变化的技能要求和社会对包容性和敏捷性的需要正在共同塑造新 的劳动力群体。
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".