Differential effects of global modifications to large‐scale high stakes examination programmes
Bibliographic record
Abstract
Driven largely by calls for accountability, the use of large‐scale testing is expanding in terms of the number and purposes of testing programmes. At the same time, financial constraints have resulted in attempts to reduce the lengths of such examinations. An examination of the 1994/1995 and 1995/1996 British Columbia Scholarship programme illustrates that differential and unanticipated differences can occur when such changes to the testing programme are made. The removal of a portion of the constructed‐response (CR) and written tasks (WT) items used to identify scholarship recipients resulted in differences in scholarship scores and the identification of scholarship recipients. Further, the differences were found to affect subgroups of students differentially. While there were no differences attributed to gender, higher difference rates were associated with course area (humanities vs. science) or examination session (January vs. June). The results illustrate the complex and contextual impact of changes to examination programmes and the potential consequences of such changes. Test developers and users must make more of an effort to examine the consequences of examination programmes and planned changes upon the students and others who may be affected by the results.
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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 | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".