Examining the affordances of social network sites as a tool of military strategic communication in a hybrid media environment.
Bibliographic record
Abstract
In recent years military actors have extensively embraced the opportunities digital networked technologies have presented to communicate directly with target audiences and to engage in sophisticated impression management. This dissertation research project examines how social network sites (SNSs) intersect with and augment strategic military communication practices using a communicative affordances framework. Specifically, the research aims to understand how SNSs are integrated into military strategic communications practices as a means to meet their broader institutional goals whilst situating the research within the complex hybrid media ecology in which this communication takes place.The project employs a mixed-method research approach of in-depth interviews with communications professionals working within the military and defence sectors across Ireland, Canada, the United States, Germany, the Netherlands, Israel and NATO, combined with a multi-modal thematic analysis of timeline images uploaded to the official Israeli Defense Forces Facebook page over a period of three years. The visual thematic analysis of an active military institution at the forefront of utilising SNSs, compliments the analysis of the semi structured interviews, providing vital context and a more holistic understanding of the potential affordances and limitations of SNSs as a tool of military strategic communication. The research findings provide in depth insights about how SNSs are perceived and used by military communications actors as a platform to articulate their activities and legitimacy to multiple target audiences and to build public support. The study employs the theoretical framework of social media affordances to conceptualise how SNSs augment practices of military strategic communication. The findings reveal a significant shift in the nature and tone of military strategic communications made uniquely possible by the affordances, norms and vernaculars of SNSs. This empirical study may inform broader theories about the mediatisation of the military in a hybrid media ecology and contribute to a conceptual typology for the study of visual military communication practices online.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".