Bibliographic record
Abstract
This paper addresses how the Socio-Economic Approach to Management (SEAM), an intervention research method (Cappelletti, Savall and Zardet, 2024), can transform new risks and costly dysfunctions faced by contemporary organizations, into added value and enhanced organizational performance. This process is increasingly important due to the “profound upheaval” (Savall, 2018) of economies that sociologist Ulrich Beck (1992) has termed Risk Society. Risk Society (RS) refers to 1) a sociological theory describing a new, emergent, form of social order and 2) the risk society itself. Risk society is the outcome of decades of the unbridled wealth production that created damaging social, economic, and environmental side effects, e.g. pollution, that are costly or difficult to manage with traditional tools. The Socio-economic Approach to Management is an organization change intervention approach that provides a means to address these unwanted side effects using new tools to convert these risks and dysfunctions into value added. Risk society theory, unfortunately, offers few actions to overcome the new risks RS brings. SEAM, however, provides new and proven tools to address these unwanted side effects of risk society. These new tools are described and their potential for managing the new risks that have emerged is demonstrated.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".