Cutting the Cord: Mutual Respect, Organizational Autonomy, and Independence in Organizational Separation Processes
Bibliographic record
Abstract
Based on a longitudinal, qualitative analysis of developments in the English National Health Service, we develop a process model of how organizations divest or spin off units with the aim of establishing two or more autonomous organizational entities while simultaneously managing their continued interdependencies. We find that effective organizational separation depends on generating two types of respect—appraisal and recognition respect—between the divesting and divested units. Appraisal respect involves showing appreciation for competence or the effort to achieve it, while recognition respect requires considering what someone cares about—such as values or concerns—and acknowledging that they matter. The process model we develop shows that open communication is crucial to the development of both. We also find that certain attempts to gain organizational independence and respect may unintentionally undermine the development of autonomy. Counterintuitively, we find that increasing or maintaining interorganizational links via communication may facilitate organizational separation, while attempts by units to distance themselves from one another may unintentionally inhibit it. By linking organizational separation, autonomy, independence, and respect, this paper develops theory on organizational separation processes and more generally enhances our understanding of organizational autonomy and its relations with mutual respect.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".