Fans, Friends, Advocates, Ambassadors, and Haters: Social Media Communities and the Communicative Constitution of Organizational Identity
Bibliographic record
Abstract
Organizational identity is always somewhat socially co-authored. The social-media context provides an opportunity to interrogate the extent of this co-authoring in an interaction-heavy and difficult to control environment. This article presents a typology of online communities that co-author organizational identity through confirming and disconfirming identity messages. Through extensive qualitative research, including interviews, marketing meetings observations, and social media interaction observations, social media communicative practices are examined through a communication constitutive of organizing (CCO) framework, specifically the conversation-text dialectic of the Montreal School. By focusing the research on boundary-spanning social media marketers and their interpretations of social media interactions, this article demonstrates ways that organizational identities are co-authored from external interaction (conversation) to internal practice (text). This study contributes to the ongoing theoretical extension of the CCO framework beyond the container metaphor, while also contributing to the practice of social media marketing within and around organizations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".