Speaking in Unison: The Voice Dilemma in Open Strategy
Bibliographic record
Abstract
In this paper, we adopt a Communication as Constitutive of Organizations (CCO) perspective to investigate how organizations implementing Open Strategy initiatives maintain openness and closure in tension by attending to a plurality of voices and their diversity (polyphony), while at the same time speaking in one strategic voice (monophony). Based on the Kiabi case, we explore what we name the voice dilemma by focusing on the ways different stakeholders involved in strategy making manage the co-authoring of strategy through voicing, negotiating, and legitimizing matters of concern. We contribute to extant literature by focusing on the management of polyphony and monophony as a way to embrace the paradox of openness that characterizes Open Strategy. More precisely, we show how some form of closure needs to be nurtured during the opening process (the co-authoring process during which multiple employees are invited to contribute to strategizing). However, we also argue that some form of opening needs to be nurtured during the closure process (the process during which the official authoring/positioning of the organization is finally defined). This study offers a longitudinal case that allows showcasing how the opening and closing strategies evolve over time.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".