The Myth of more Social Inclusion through Activation Reforms - The Case of Germany
Bibliographic record
Abstract
The paper presents new findings on a specific 'gendered' problem resulting from 'activation policies' and a certain group of unemployed which has been widely neglected so far in public and academic discourse although it is both quantitatively significant and reveals systematic failures of activation strategies: In contrast with claims of greater social inclusion through 'activation, it can be seen that currently nearly a quarter of a million people in Germany are registered as unemployed without any entitlement to unemployment benefits or any individual social protection like health care. These 'unemployed non-beneficiaries' (UNB), currently account for a quarter of those registered as unemployed within the statutes of the German Social Code Book III (SGB III). Their situation is problematic for two reasons: First, their significant number is evidence of a severe social security gap which has existed before the 'Hartz reforms' but has grown more acute by the new, stricter eligibility rules to unemployment benefits introduced with the reforms. This re-familisation of social security stands in contrast to the more precarious employment structures on the labour market on the one hand, and the more egalitarian gender norms of an individualised society on the other. Second, our recent research findings show that unemployed non-beneficiaries are practically even excluded from active employment promotion services of the Federal Employment Agency (Bundesagentur für Arbeit, BA), despite the fact that they have the same obligations to prove active job search and mostly are not able to find jobs by themselves. The German activation regime even systematically pushes them to de-register as unemployed (to 'improve' the labour market statistics). The paper argues that this can be explained by the business management principles of cost-efficiency introduced with the 'Hartz reforms' as the primary guideline for the Federal Employment Agency. Performance measurement tools which prioritize short-term job insertion and the cost-efficient allocation of the BA-budgets to the more 'marketable' groups of unemployed override social, labour market and political equality targets. This is detrimental for vulnerable groups like unemployed non-beneficiaries, and also contrary to the macro-economic goal of counteracting the reputed skill shortages. The paper is based both on secondary analyses of official statistics and own empirical panel data (GSOEP) and some qualitative findings, derived from own research projects funded by different sources.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".