Escalating Indecision: Between Reification and Strategic Ambiguity
Bibliographic record
Abstract
This paper examines an organizational pathology that we label “escalating indecision”—where people find themselves driven to invest time and energy in activities and decision processes aimed at resolving an issue of common concern, but where closure appears elusive. The phenomenon is illustrated through a case history in which a strategic orientation decision involving the configuration of a group of large teaching hospitals was continually made, unmade, and remade, producing little concrete strategic action over many years before achieving more tangible moves toward implementation. The paper introduces the notion of a “network of indecision” in which participants have become sufficiently attached to a common project to continue working together to move it forward, but their divergent conceptions of what this involves prevent them from materializing it in a tangible form. The paper suggests that networks of indecision are dialectically constituted through a set of practices of reification and practices of strategic ambiguity. The phenomenon is strongly associated with pluralistic settings characterized by diffuse power and divergent interests, and its prevalence is likely to be greater in situations of reactive leadership, uncertain resource availabilities, and long time horizons.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| 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".