Bibliographic record
Abstract
Shin, Dongil & Park, Gina. “Exploring the misuse of language educational rights: In the context of high-stake test preparation for elementary school students.” Studies in English Language & Literature. 38.4 (2012): 323-351. The purpose of this study is to understand the Korean young English language learners (YELL) and test-takers studying TOEFL-iBT from the perspectives of human rights. The term 'human rights' is commonly associated with a legal or political concept, but it is also discussed in casual and everyday situations of life. This study tries to identify the mismatch between what has been documented from the literature in YELL and human rights, especially enunciated through international organizations, and what is really conventionalized in English language education in Korea. Interview data was obtained to explore the human right issues among young learners and test-takers of English in Korea. Three participants who have been preparing for the TOEFL-iBT were all sixth-grade female students. It was found that participants' rights of children, language learners, and test-takers were not fully valued in their TOEFL-iBT preparing contexts. It is hoped that this study would lead to awareness-raising movements for the children's rights in the field of English language learning and testing. It would raise the awareness of why the issues of human rights and ethics are needed to enlarge the related boundaries of professionalism in English language education. (Chung-Ang University, University of Toronto)
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".