Repurposing Institutions through Categorization: New Apprenticeship Programs in Peripheral Service Industries
Bibliographic record
Abstract
The rise of precarious work is an increasing concern for policymakers and researchers, with outsourcing frequently identified as a key driver. In response, skills development and formal qualifications are widely promoted as potential remedies. We examine this development through a categorization lens, focusing on two vocational credentials created for industrial services in Germany. By analyzing how industry actors categorize the work of apprenticeship-qualified employees, we investigate whether recategorizing peripheral jobs as skilled labour effectively addresses precarious employment. We find that the two apprenticeship programs reframe work as skilled and legitimate without substantially improving conditions for workers. Employers may repurpose apprenticeships to serve commercial interests rather than traditional worker-centred roles. This illustrates how institutions can be incrementally reshaped and even undermined through the social negotiation of occupational categories. We demonstrate the applicability of categorization theory to labour markets and work organization, thereby clarifying how recategorization functions as a mechanism of gradual institutional change in precarious sectors. We also show how new apprenticeship programs are strategically positioned within occupational hierarchies, with mixed implications for vocational education reform. Overall, categorization is a contested social process shaped by power asymmetries. While apprenticeships hold symbolic value in elevating work status, the actual reduction of precarity remains limited. The challenges of precarious employment are reinforced rather than resolved.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".