Disruption and re-regulation in work and employment: from organisational to institutional experimentation
Bibliographic record
Abstract
This article proposes experimentation as a framework for understanding actor agency in the changing regulation of work and employment. This involves contrasting institutional change with organisational and institutional experimentation approaches in order to understand how, in the context of uncertainty, actors in the world of work experiment with new ways of organising and seek to institutionalise them into new understandings, norms and rules. The article describes the fault lines of disruption that are generating a vast range of experiments in the world of work. These fault lines invite resilient responses and the development of collective capabilities at two levels: first, organisational experimentation, where social actors seek to modify or renew their organisations, networks and alliances and reflect on, assess and learn from their experiments; second, institutional experimentation, where these responses are scaled up and institutionalised over time through more general understandings, norms and rules. A key challenge for comparative research and strategising is to find the appropriate institutional conditions that will facilitate and enable organisational experiments, whilst overcoming constraining institutional conditions. This challenge is illustrated through the examples of co-working and the development of new forms of collective representation.
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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 | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.106 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".