Review of doctoral research in language assessment in Canada (2006–2011)
Bibliographic record
Abstract
This paper reviews a selected sample of 24 doctoral dissertations in language assessment (broadly defined), completed between 2006 and 2011 in Canadian universities. These dissertations fall into five thematic categories: 1) reliability, validity and factors affecting test performance; 2) washback (impact) and ethics; 3) raters, rating and rating scales; 4) classroom-based research: teaching, learning and assessment; and 5) vocabulary learning, lexical proficiency and lexical richness. The themes were categorized according to the International Language Testing Association (ILTA) bibliographical categorization index. We identify trends such as the methodological strength of complex mixed methods research design, which enhances the validity of the research findings: 16 (67%) took a pragmatic (rather than paradigmatic) approach in their use of mixed methods, with four (17%) opting for multi-method quantitative approaches and four (17%) for qualitative. We also discuss the depth and breadth of these dissertations and situate their scholarly contributions within Canadian and international research on language assessment.
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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.029 | 0.064 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".