Bibliographic record
Abstract
이 연구의 목적은 외국의 과학 교육에 나타난 탐구기준 중 관찰과 측정 기준을 분석하는 것이다. 이를 위하여 미국 과학 교육 기준, 미국 캘리포니아주, 메사츄세츠주, 콜로라도주, 텍사스주, 네브라스카주, 버지니아주, 플로리다주, 영국, 싱가포르, 캐나다 앨버타주, 호주 빅토리아주, 뉴질랜드의 과학교육 과정에 나타난 탐구 기준을 분석하였다. 분석결과 다음과 같은 것을 발견하였다. 외국 교육 과정은 관찰 요소에서 관찰의 정확성, 관찰 과정에서의 신중함과 체계성, 오감의 사용, 관찰 도구의 사용, 관찰에 대한 이해의 5가지 특징적인 세부 기준을 포함하고 있었다. 또한 측정 요소에서는 측정의 체계성과 정확성을 강조하고, 측정 대상을 명시하였으며, 측정 도구를 학년별로 제시하였고, 측정에서 적합한 단위의 사용을 강조하는 4가지 특징을 가지고 있었다. 【The purpose of this study was to examine the features of the standards of observation and measurement in foreign national science curriculums. For the purpose of this study, we analyzed the science standards of the USA (National Science Education Standards, the Science Standards of the State of California, Massachusetts, Colorado, Nebraska, Virginia and Florida), the United Kingdom (England), Singapore, Canada (the State of Alberta), Australia (the State of Victoria) and New Zealand. The results of the study indicated that foreign national science curriculums put an emphasis on accurate and systematical observation, usage of the five senses, usage of observation tools, and an understanding of observation. Regarding the 'measurement' factors, foreign national science curriculums emphasized systematic and accurate measurement. Measurement targets and tools were presented for each grade. The usage of appropriate units was also included in the foreign national curriculums which we examined.】
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".