A Social Design Approach: Enhancement of Local Social Dialogue on the Transformation of Work by Digital Technology
Bibliographic record
Abstract
The world of work is undergoing major transformations (teleworking, new technologies, Industry 4.0, social reform in some countries) in which labour relations are likely to play a central role. In this context, our case study presents an alternative approach to local social dialogue: “Social Design.” The specific aim was to mobilize stakeholders to deal with the introduction of digital technology at a large industrial company in France. Within the theoretical and methodological framework of activity-centred ergonomics, we analyzed the process of co-design and the process of design “in use.” We conducted interviews, work activity observations and simulations of future working conditions. We identified “fruitful possibilities” (e.g., more extensive participation by stakeholders and collective discussions about the transformation of work) and “real-life resistance” (e.g., difficulties in finding common agreement). We report on the quality of local social dialogue and provide an epistemology of the action of social dialogue on the theme of the transformation of work. In sum, we describe an original initiative to transform local social dialogue in the context of a changing workplace.AbstractWe present the results of a research-action initiative to strengthen participation by social dialogue stakeholders (union representatives, managers and workers) in companies that are being digitally transformed. For this, we used activity-centred ergonomics. After presenting a co-design process, i.e., “Social Design,” we describe how the initiative was carried out in a large industrial company and how it was re-designed “in use.” We thus helped certain union representatives participate in dialogue on the topical issue of digital transformation, thereby helping define a new organizational structure in the workplace and further developing the “Social Design” approach.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".