Bibliographic record
Abstract
The survey "Level of Living EU-SILC 2013" is conducted by Statistics Norway. Statistics Norway has since 1996 conducted surveys on working conditions (1996, 2000, 2003 and 2006), housing, community and leisure (1997, 2001, and 2004), and health (1998, 2002 and 2005). As of 2003, the panel study altered to be included in EU-SILC (Statistics on Income and Living Conditions), a collaboration organized by Eurostat. The panel survey has been conducted in the first quarter and has a rotating main topic. Leisure and outdoor activities, as well as organizational activity, political participation and social networks are covered in 2011, 2014 and 2017. Housing and living conditions as well as vulnerability to turmoil and crime are being covered in 2012, 2015 and 2018. Physical activity and cultural activities will be covered in 2013, 2016 and 2019. Like the main topics, the sample is also rotating. In 2013 there was a particular focus on sports and cultural activities. Together the surveys on outdoor-, sports- and cultural activities, made up the theme on leisure time activities. From 2011, these different activities are mapped out different years, but gathered they cover the theme on leisure time activities. The surveys on sports and cultural activities aim to map out different aspects which are significant for the level of living, like types of activities and frequency, in addition to the share of the population who exercise all together.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".