Social Media Usage of Non-Governmental Organizations: Red Cross Societies of the G7 Countries and the Turkish Red Crescent
Bibliographic record
Abstract
Nowadays, with the emergence of the internet concept and its continuous development, non-governmental organizations have started to use these channels to maintain their existence and to promote themselves. In this study, it is examined how non-governmental organizations in developed countries and Turkey use their Twitter accounts. The study covers a period of six months (from May 1 to October 31, 2019). The universe of this study consists of all Red Cross organizations in developed countries and the Turkish Red Crescent. The sample of the study consists of Germany, Italy, France, the USA, England, Japan, and Canada, which are G7 countries and hold the majority of global wealth. In the scope of the study, G7 countries and the Turkish Red Crescent will be compared. As a result of the review, it is reached that the Turkish Red Crescent ranks first in the number of posts on Twitter and uses visual icons like other Red Cross organizations in the study. Moreover, similar to other institutions, the Turkish Red Crescent does not go into time and day limitations in their posts. It can be said that all the NGOs under the study used this medium effectively; however, the biggest deficiency is that they are not successful in answering the comments for the posts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".