Bibliographic record
Abstract
Purpose The purpose of this paper is to explore and expand the role of strategic ambiguity (SA) in the field of organizational communication. It treats the triple bottom line (TBL) as indicative of an emerging coalition. This coalition brings together three loosely coupled discourse communities each attempting to advance the notion of green business, corporate social responsibility and sustainability. Design/methodology/approach This case directs attention to how SA and equivocation built into TBL aids three loosely networked discourse communities – formulated around “profits”, “people” and “planet” – emerge, coalesce and diffuse despite being rooted in imprecise and loosely formulated measures. Findings The findings indicate that despite its imprecision, lack of specificity and operational indices the TBL provides its members with the belief that they are far better off joining the coalition than going it alone. TBL's openness to multiple interpretations enables each of the discourse communities in the emerging network to expect to win concessions from others and to protect its values from encroachment. Originality/value This treatment of TBL suggests that SA can be expanded beyond an intra‐organizational focus to one encompassing emergent coalitions. The expanded notion of SA helps explain the stickiness of knowledge transfer in the early stage of coalition formation and the propensity of critics to view new imprecise but inspiring ideas like TBL as nothing but a fad or passing enthusiasm.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| 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".