The Origins and Dynamics of Organizational Resilience: A Comparative Study of Two French Labor Organizations
Bibliographic record
Abstract
Why are some organizations more resilient than others? This paper argues that the cultural repertoire and structure of organizations go a long way toward accounting for variation across organizations. It hypothesizes that organizations endowed with a heterogeneous repertoire, a centralized structure, and autonomous leadership are more likely to be resilient. In addition to identifying these two antecedent conditions, this paper unpacks the dynamic of organizational resilience and highlights three overlapping processes: narration (how actors make sense of their environment and insert their representation in a causal chain of events unfolding over time and pointing to a particular path), learning (how organizations produce and encode knowledge into routines that guide behavior), and institutionalization (how the leadership imposes and infuses with value a particular narrative and strategy within the organization). In order to substantiate this argument, this paper compares the trajectories of the two largest labor organizations in France, the CFDT and the CGT. While the former reacted quickly to decline and demonstrated a relative resilience, the latter proved incapable of responding adequately to changing circumstances and drifted for a long period before it finally tried to change course. This paper is based on documentary research in the archives of these labor organizations and on semi-structured interviews with labor leaders and union staff.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".