PENGARUH KAMPANYE ‘DOGS ARE NOT FOOD’ YANG DILAKUKAN DI AKUN TWITTER @ALBERTHIENEE TERHADAP PEMAHAMAN MASYARAKAT
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Bibliographic record
Abstract
Social media users today are not only use their account for communicate and interact with other people, but also use it for a campaign tool. Alberthiene Endah uses her Twitter account to attempting ‘Dogs Are Not Food' campaign, also to increasing people's awareness and cognitive concerned dogs are definitely not human's food. This study aims to determine how ‘Dogs Are Not Food' campaign could influence people's cognitive. The data analytical uses quantitative approach and multiple regression test with two independent variables, namely Volume (X1) and Engagement (X2), also dependent variable namely cognitive (Y). The samples gaining 263 respondents whom followed @AlberthieneE's Twitter account.The F-Test derived obvious influence between volume and engagement towards people's cognitive simultanously. Then the T-Test derived obvious influence either volume or engagement towards people's cognitive partially. According to determination coefficient, Adjusted R Square result points out 0,482, which means the variation of dependent variable can be explained by independent variables as much as 48,2%, the rest (51,8%) is influenced by another factors. According to regression coefficient result, X1 points out 1,047 meanwhile X2 points out 0,488. It examines that coefficient X1 is higher than X2, which means people's cognitive concerning 'Dogs Are Not Food' campaign strongly more influenced by volume than engagement. The overall result, it stated the better assessment from people about volume and engagement @AlberthieneE Twitter account, the better people's cognitive concerning 'Dogs Are Not Food' campaign also. Keyword: social media, Twitter, campaign, people's cognitive, Alberthiene Endah, ‘Dogs Are Not Food' DAFTAR PUSTAKA Adler, E. S., & Clark, R. (2011). An Invitation to Social Research: How It's Done (4th ed.). Belmont: Wadsworth-Cengage Learning. Akun Twitter @AlberthieneE. 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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it