Prediksi Pengembangan Diri Masyarakat dalam Masa Quarter Life-Crisis melalui Instagram Stories
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menguji dan membuktikan secara empiris: 1) Prediksi pengembangan diri masyarakat melalui fungsi Instagram; 2) Prediksi pengembangan diri masyarakat melalui Quarter Life-Crisis; 3) Prediksi pengembangan diri masyarakat melalui fungsi Instagram Stories dan Quarter Life-Crisis secara simultan; dan 4) dan prediksi pengembangan diri masyarakat melalui fungsi Instagram Stories dengan dimediasi oleh Quarter Life-Crisis. Diharapkan hasil penelitian ini secara teoritis dapat bermanfaat bagi akademisi dan praktisi, terutama untuk menambah ilmu pengetahuan di bidang ilmu komunikasi.Penelitian ini menggunakan metode penelitian kuantitatif dengan teknik purposive sampling dengan jumlah sampel 100 responden. Metode pengumpulan data dilakukan dengan cara penyebaran kuisoner, wawancara langsung dan dokumentasi. Data dianalisis menggunakan software SPSS 27 for windows. Hasil penelitian ini menunjukkan: 1) Prediksi positif dan signifikan pengembangan diri melalui fungsi Instagram; 2) Prediksi negatif dan signifikan pengembangan diri melalui Quarter Life-Crisis; 3) Prediksi positif dan signifikan pengembangan diri melalui fungsi Instagram Stories dan Quarter Life-Crisis secara simultan; dan 4) Efek mediasi yang signifikan dari Quarter Life-Crisis pada fungsi Instagram Stories dan pengembangan diri.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".