Bibliographic record
Abstract
学习评价一直是各国教育改革与实践的＂新宠＂。加拿大亚伯达省（Alberta）的学生学习评价（SLA）是以加拿大的社会走向为背景,政治、经济、教育与师生的相互作用为动力因素而产生的学习性评价,是对传统教育评价的传承与发展,又自然融入了＂启发性教育＂和学生未来发展评价的深度检视。在实践过程中发挥学生学习评价的工具性价值,在目的和功能走向上侧重发展性价值。反观我国学习评价的现状及特点,建构由静态教育评价转为生本特征的学生学习评价,数值化指标转为增值能力的指标系列和散乱的评价块转为创生指导的评价方案,力求填补我国学习评价领域的空缺。
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".