Bibliographic record
Abstract
Social media networks are the most important product of the development of computer and communicationtechnologies that affect social life. Social media networks have become a driving force in social and culturaldevelopment, while providing social contact for people. This force has improved its sphere of influence oversocieties in many fields such as health, defense, banking, commerce, marketing and entertainment, especially ineducation, which sometimes have no relationship with each other. This study is a qualitative educational researchbased on content analysis of teacher candidates' research on using social media networks. The study's population iscomposed of 552 teacher candidates who are reached with the help of social media networks. A data collection tooldeveloped by the researcher in order to collect data was used in the research. A personal information sectioncontaining information on the participants and their use of social media networks was used in the first part of the datacollection tool while a form consisting of 7 semi-structured questions was used in the second part. Data wereanalyzed by using descriptive analysis and content analysis for the data obtained from data collection tool. Given thefindings of the study, it is concluded that more than half of teacher candidates participating in the research use socialmedia networks more than once every day; more than half of these candidates use social media networks for 2 to 4hours a day; they mostly use mobile instant messaging tools; the most popular social media networks teachercandidates are Instagram and Facebook; they mostly use social media networks in order to communicate with theirfriends; they attribute different meanings to social media networks and they regard social media tools as apedagogical value.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".