Paper Presented at the Annual Meeting of the National Council on
Bibliographic record
Abstract
Achievement tests are administered routinely in multiple languages to students throughout the world. For example, the International Association for the Evaluation of Educational Achievement (IEA) conducted the Third International Mathematics and Science Study in 1995. The tests were administered in 30 different languages to students in 45 participating countries (Hambleton & Patsula, 1998). Similarly, the Organization for Economic Co-operation and Development (OECD) conducted the Programme for International Student Assessment (PISA) in 2000. Tests of reading, mathematical literacy, and scientific literacy were administered in 13 different languages to students in 32 participating countries (Grisay, 2002). Hambleton (1994), Hambleton and Patsula (1998) and Sireci (1997) contend this trend toward multilingual testing will continue because of an increase in the international exchange of tests, a growing demand for credentialing and licensure exams in multiple languages, the cost efficiency in procuring adapted tests compared with constructing new tests, and a growing interest in cross-cultural research. Increasingly, multilingual tests are also being administered with alternative testing procedures. For example, the School Achievement Indicators Program (SAIP) in Canada uses two-stage
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".