Analysis of Alienation in Informal Education: Media Skepticism and Spiral of Silence in the Network Society
Bibliographic record
Abstract
In the study, the alienation in the network society is investigated. Facebook, which is highly effective among network community applications, has been examined as an informal learning tool. In this context, the topic of learning is "political, social, religious, cultural contents that society is sensitive". The research was conducted with the participation of university students who are members of the network society. Spiral of silence (SoS) was taken into account as a sign of alienation. It has been examined whether the media skepticism is effective in solving the problem of alienation. In this context, the relationship between spiral of silence and media skepticism has been investigated. As a result of the research, it was understood that young adults who are university students are in the spiral of silence in sharing about "political, social, religious, cultural contents, society is sensitive" and therefore alienation exists. In the context of media skepticism, participants' skepticism to others' posts is high, skepticism to self posts is low. While there is a significant, negative and low level of correlation between spiral of silence and skepticism to others posts, there is no significant relationship between spiral of silence and skepticism to self posts. There is a potential for skepticism to others' posts to be effective in resolving alienation in network society.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".