Bibliographic record
Abstract
This special issue brings together five articles on the Survey of Adult Skills (PIAAC).The PIACC measures the proficiency of 16-65 year-olds in literacy, numeracy and problem solving in technology-rich environments.The main motivation for the survey is that these cognitive skills are critical inputs for success in the labor market.There were two rounds of data collection for the PIACC.In the first round, which took place during 2011 and 2012, a total of 166,000 adults between the ages of 16 and 65 were surveyed in 21 countries: Australia, Austria, Belgium (only Flanders), Canada, Cyprus, the Czech Republic, Denmark, England (and separately Northern Ireland), Estonia, Finland, France, Germany, Ireland, Italy, Japan, Korea, the Netherlands, Norway, Poland, Russia, the Slovak Republic, Spain, Sweden and the United States.The second round took place during 2014 and 2015 in 9 additional countries with a total of 50,250 adults.The countries that participated in the second round were Chile, Greece, Indonesia, Israel, Lithuania, New Zealand, Singapore, Slovenia and Turkey.OECD (2016) provides details on the survey and an overview of the key facts.Table 1 documents the levels of literacy and numeracy proficiency across the OECD countries.The literacy outcomes measure "the ability of individuals to understand, evaluate, use and engagex with written texts in order to participate in society, achieve one's goals, and develop one's knowledge and potential" (OECD 2016, p. 38), while numeracy measures "the ability to access, use, interpret and communicate mathematical information and ideas in order to engage in and manage the mathematical demands of a range of situations in adult life" (OECD 2016, p. 47).The maximum score is 400 in for both measures and the table report average scores for each country.There is a large variation in the average levels of proficiency of literacy and numeracy across
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.109 | 0.095 |
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".