European Jobs Monitor 2014: Drivers of Recent Job Polarisation and Upgrading in Europe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
[Excerpt] European labour markets added nearly 30 million new jobs in a golden age of employment creation prior to the onset of the Great Recession in 2008. These labour markets subsequently shed six million jobs, and unemployment peaked at 11% in 2013, its highest rate in well over a decade. This third annual European Jobs Monitor report looks in detail at recent shifts in employment at Member State and European Union level in the two years from the second quarter of 2011 to the second quarter of 2013. It applies a jobs-based approach, which ranks jobs according to wage and then groups them into five categories of equal size (quintiles) ranging from lowest-paid to highest-paid. The net employment change between the starting and concluding periods (in terms of people employed) for each quintile in each country is summed to establish whether there has been net gain or loss. This analytic approach enables employment shifts to be described quantitatively (how many jobs were created or destroyed) and qualitatively (what sectors and occupations were most affected). The report also examines some of the likely drivers of recent shifts in the employment structure: technological advances, as measured by the cognitive and routine task content of jobs; globalisation and trade, measured as the offshorability of tasks or direct international trade; and labour market institutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it