Understanding structures and practices of meaning-making in industrial networks
Bibliographic record
Abstract
Purpose This paper aims to propose an approach for exploring industrial marketing network environments through a social semiotic lens. Design/methodology/approach This conceptual paper introduces social semiotic perspectives to the study of business/industrial network interaction. Findings This paper describes how structures of meaning derived from a cultural history of signification and interpretive processes of meaning in action are co-determined in social semiosis. The meaning of environments using this social semiotic approach is emphasised, leading us to explore the idea of the “atmosemiosphere” – the most highly complex business network level, in illustrating how meaning is made through structuration between structures of meaning and their enactments in interactions between actors within living business networks. Practical Implications Figurative language plays an important role in the structuration of meaning. This facilitates establishing plots and, therefore, in the actors’ capability to tell a story, which starts with knowing what kind of story can be told. By implication, the effective networker must be a consummate moving “picture maker” and, to do so, she must have competence in narrative, emplotment, myth-making, storytelling and figuration in more than one discursive repertoire. Originality/value In using a structurational discourse perspective informed by social semiotics, our original contribution is a “business networks as discursive constructions” approach, in that discursive nets, webs of narratives and stories and labyrinths of tropes are considered just as important in constituting networks as networks of actor relationships and patterns of other activities and resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".