Bibliographic record
Abstract
LOST CONNECTIONS Uncovering the Real Causes of Depression and-the Unexpected Solutions(KAYBOLAN BAĞLAR Depresyonun Gercek Nedenleri ve Beklenmedik Cozumler) Johann Eduard Hari,(Ceviri Baris Engin AKSOY) Metis Yayincilik 2.Baski ISBN-13: 978-605-316-163-9 Bu calismada incelenecek olan eser,Britanyali yazar ve gazeteci Johann Hari'nin depresyon ve kaygi uzerine kaleme aldigi LOST CONNECTIONS Uncovering the Real Causes of Depression and-the Unexpected Solutions(KAYBOLAN BAĞLAR Depresyonun Gercek Nedenleri ve Beklenmedik Cozumler) adli kisisel gelisim kitabidir. Hari,kendi hayatinin da buyuk bir parcasi olan depresyon ve kayginin dokuz nedenini okuyucuya sunmaktadir.Depresyon ve kayginin kulturel bir sorun oldugu bunun da bireyi meta haline donusturmesinin vurgusunu carpici ve gercekci bir yontem kullanarak kaleme almistir.Bu yontem sayesinde okuyucuyu dusundurmeyi basarmistir. Her yasta depresyon ve kaygi sorunu yasayan bireyleri psikiyatrik ilac tedavisi kullaniminin koklu ve basarili sonuclar dogurmadigi,kisilerde meydana gelebilecek iyilesmelerin dissal sebeplerden ote icsel sebeplerde irdelenmesi gerektigi hususunda yonlendirmektedir.Bu eser atalarimiz ile benzer gereksinimler gostermemize ragmen gelecekteki degisikliklere nasil adapte olacagimizin yolculugudur.Yazar da ciktigi bu yolculuk boyunca depresyon ve kaygi yasayan bircok insanla gorusmus,hayatlarindan kesitleri aktarmistir.Bununla birlikte bilimsel makale,tez ve deneylerden yararlanmis bu bilimsel calismalara kitabinda yer vermistir. Dili yalin,anlatimi surukleyici eserinde uzun sureli kimyasal uyaricilarin(ilac,hap)zararli etkilerine karsi bizleri uyarmaktadir.Kitaptaki soylesilerin ses kayitlarinin bulundugu internet adresi sayesinde kanitlanabilirligi artmaktadir.Kitabin sonunda kapsamli bir kaynakca bolumu ardindan tesekkur ve dizin bolumu gelmektedir.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.018 |
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".