Protecting Social Inclusion and Mobility in a Low Growth Scenario. WWWforEurope Working Paper No. 100
Bibliographic record
Abstract
The unusually rapid economic growth during the second quarter of the 20th century led to a marked reduction of sustained exclusion and discrimination against groups of persons. However, in the 1980s and 1990s, growth rates in industrialised countries began to drop; the different types of real and, more importantly, perceived social exclusion once again increased and became socially critical. The economic policy of the EU and its member countries has, however, not dealt systematically with the problem of exclusion, instead addressing it only in isolated, individual steps, with the main focus being efficiency and the bolstering of growth. This, however, cannot be expected to take place to such an extent that a "trickle down" effect would automatically reduce exclusion; there must be an active and innovative anti-exclusion policy. This report attempts to outline which problems have been neglected in the WWWforEurope concept, for which problems solutions have been proposed, and for which problems solutions still need to be worked out.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".