The Proof of the Pudding… A Response to the Sticht-Murray Debate about IALS and ALL
Bibliographic record
Abstract
When two such eminent researchers as international consultant Thomas Sticht and statistician Scott Murray see the International Adult Literacy Survey (IALS), and the more recent Adult Literacy and Lifeskills (ALL) Survey, so differently what is a literacy advocate to believe? I am writing to make sense of this divergence and I argue that both Sticht and Murray provide us with some of the ingredients but the proof is in the end product. According to the ALL survey, 42 % of adult Canadians have inadequate literacy skills for today’s society. These figures are essentially unchanged from the 1994 IALS findings. With the release of the 2005 results, literacy groups in Canada scrambled to explain the lack of progress since 1994. They fed the results into public awareness campaigns and used them to lobby politicians at both provincial and national levels in an effort to increase support for literacy programs. They renewed the call for a national literacy strategy to address the literacy ‘problem’. Sticht and Murray disagree on the significance of the survey findings. Sticht argues that the Response Probability (RP) of 80 % used in analyzing the results is too stringent and that a Response Probability of 50 % would be both more accurate and realistic; this would halve the number of Canadians placed in Levels One and Two. He also questions the choice of Level
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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 | 0.068 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.042 | 0.091 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".