Communication Ecosystem Contexts: From Mass Audiences to Mass Messages
Bibliographic record
Abstract
Abstract Mass media used to have ‘mass audiences’. Nowadays, and with the emergence of the Internet and other new media technologies, messages themselves have turned into ‘mass’. These ‘mass messages’ have different densities, varieties, and dynamics in different societies and different countries. Accordingly, in this paper, we take an ecological perspective and resemble mass messages to living organisms that form ‘communication eco-systems’. We then introduce ‘communication ecosystem context’ as a new umbrella concept encompassing various previously known communication contexts. Based upon historical evidences, communication ecosystem contexts are classification into long-term and short-term, which can be formed normally, forcefully, or due to turn of certain events. In this paper the impacts of communication ecosystem contexts on message perception and interpretation have been studied primarily in Iran, and comparison have been made with five different communication ecosystems of Germany, UK, Australia, USA and Canada.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".