Intelligent Use of Intelligence Tests
Bibliographic record
Abstract
It is well established that Canadians produce higher raw scores than their U.S. counterparts on intellectual assessments. As a result of these differences in ability along with smaller variability in the population’s intellectual performance, Canadian normative data will yield lower standard scores for most raw score points compared to U.S. norms. Two recent studies have questioned the utility of the WAIS–IV Canadian norms based on the performance of a mixed clinical sample of post-secondary students. These studies suggest that a greater proportion of cases from their mixed clinical samples fall below a full-scale IQ of 85 using the WAIS–IV Canadian norms than should be “expected.” The purpose of the current study is threefold: First, to summarize the consistent finding of Canada–U.S. differences on measures of ability and present new empirical analyses to demonstrate these results are not due to a smaller sample size for Canadian norms. Second, and most importantly, matched sample comparisons demonstrate that the proportion of low scoring individuals (FSIQ < 85) in mixed clinical samples is consistent with the rates published by recent studies, and not greater than expected. Third, we offer evidence-based advice to clinicians practicing in Canada on the appropriate use of Canadian norms for Canadian clients during an individual assessment of intellectual functioning.
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 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 | 0.001 | 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) | 0.003 | 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".