‘Smart’ Industrial Relations in the Making? Insights from Analysis of Union Responses to Digitalization in Italy
Bibliographic record
Abstract
How do unions respond to the emerging threats and opportunities posed by digitalization in the sphere of employment relations? What factors account for the focus and varying effectiveness of their responses? This paper seeks to address these questions in the case of Italy—a theoretically interesting case that combines significant digitalization-related challenges, historically strong industrial relations institutions under increasing pressure, and diverse union confederations. From the available evidence, we find that Italian union strategies and demands so far have been primarily focused on interventions at the macro and meso levels, with a view to extending traditional forms of protection—especially sectoral collective bargaining agreements—to deal with the disruptive effects of digitalization. This focus has been coupled with some limited innovation in union agendas and discursive repertoires focused on the micro level of intervention, as well as a shift in union preferences toward inclusion of platform workers and self-employed workers in their constituencies. Whilst highlighting the importance of agential factors, we nonetheless find that the focus and effectiveness of union interventions are crucially shaped by prior institutional legacies and distributions of power resources, as well as by the ideological orientation and strategic capabilities of individual unions themselves. Overall, Italian unions have to date tended to privilege gradual response strategies based on extension and adaptation of existing and established institutions. It remains to be seen whether such adaptive approaches will be sufficient to effectively govern the digital transformation of work or whether more radical institutional experimentation will become necessary. Either way, in order to build smart industrial relations in Italy, unions will have an active role to play.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".