Spaces of Social Recomposition: Resisting Meaningful Work in Social Cooperatives in Italy
Bibliographic record
Abstract
Abstract Workers’ experiences in Italian social cooperatives expose the tension between the goal of desirable change that these social economy organisations pursue and the demanding working conditions shouldered by staff. Born from the radical movements of the 1970s, social cooperatives deliver care and community services that seek to counter inequalities and marginalisation. This important work fuses employment with activism. Yet, cooperatives often rely on casualised labour practices that normalise overwork. A noble mission does not guarantee cooperatives will also be sustainable work environments. Based on extensive qualitative research conducted in Milan, this paper explores how, since 2013, a collective of social economy workers has been mobilising to challenge poor working conditions, query the ways workers participate in them, and connect labour demands with broader struggles against austerity and impoverishment. These organising efforts pursue social recomposition: a form of labour struggle that exceeds the workplace and embraces the sphere of social reproduction. Combining a workerist framework of class composition with feminist insights, this paper invites attention to the ways workers inhabit and struggle within, against and beyond their work.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| 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".