Bibliographic record
Abstract
No party line is required to challenge bad data, badly collected, badly analyzed, and badly reported, Ms. Robertson retorts. SINCE HE is so eager to draw attention to my organizational affiliations, let me remind readers that Gilles Fournier is the coordinator of the national testing program that he defends so vigorously. As my critique of the School Achievement Indicators Program (SAIP) had to do with its substance, rather than its provenance, I saw no reason to mention Fournier's name in my column. This may change, however, since Fournier has served up a number of quite astonishing quotable quotes that I may not be able to resist citing in future commentary on the Council of Ministers of Education Canada (CMEC) and SAIP. For example, he claims that, since I find numerous faults with the national testing program, I am obviously opposed to student evaluation of all kinds. He then makes the equally silly statement that the act of administering tests that teachers have neither designed nor marked constitutes training teachers to assess their pupils properly. Somehow I doubt that Fournier's credibility in the evaluation field has been enhanced by these remarks. Fournier defends his program by pointing to results that have found that differences in scores are associated with gender and linguistic differences. I can only hope that these results did not surprise him or his office, any more than did the finding that 16-year-olds know more than 13-year-olds. Yet even this spirited defense avoids the pretense that SAIP provides anyone with the slightest idea of how to alter persistent achievement gaps. Indeed, I note that Fournier evades entirely the matter of how six SAIP assessments have been used to inform policy and improve practice, unless designing curriculum around questions found on standardized tests constitutes policy making. With respect to Fournier's objections to my depiction of the expectations-setting process, I feel obligated to warn my critic that he does his case no favor by providing readers with more details about a process so bereft of validity and reliability. I believe others will dispute his claim that this process is consistent with the Modified Angoff, which is used to determine cutoff scores - not the percentages of students who should achieve at predefined levels. Even the 1997 review of SAIP, commissioned by CMEC, recommended changes to enhance the adequacy of the expectations-setting or standards-setting process to deal with bias and to address the validity and reliability of the expectations. Robert Crocker pointed out that, in the absence of confidence intervals associated with these expectations, there is no way to determine whether the reported differences between expected and achieved results are statistically significant. …
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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 | 0.219 | 0.501 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.032 | 0.045 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.019 | 0.066 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".