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Record W3130985321

2020 인구포럼 운영

2020· book· ko· W3130985321 on OpenAlexaboutno aff
이윤경, 이선희, 변수정, 김혜수, 진화영

Bibliographic record

Venue한국보건사회연구원 eBooks · 2020
Typebook
Languageko
FieldEnvironmental Science
TopicKorean Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicPolitical scienceMedicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

제1장 2020 인구포럼 개요 1 제1절 2020 인구포럼 개요 3 제2절 제1차 인구포럼 개요 4 제3절 제2차 인구포럼 개요 6 제4절 제3차 인구포럼 개요 8 제2장 제1차 인구포럼: 젠더관점에서 저출산 대응을 위한 사회구조 분석 11 제1절 여성의 고용 안정성과 출산율 13 제2절 저출산기본계획에 대한 젠더 분석-저출산 담론의 재구성을 위하여 26 제3절 종합 토론 49 제3장 제2차 인구포럼: 세대 공감 53 제1절 기조강연 55 제2절 소득·일자리에서의 세대 갈등과 대안 66 제3절 사회문화에서의 세대 갈등과 대안 84 제4절 종합 토론 105 제4장 제3차 인구포럼: 감염병 확산에 따른 국내외 노인 돌봄 환경 변화와 대응방안 121 제1절 Policy responses to COVID-19 in long-term care for senior in Canada 123 제2절 Underfunded and unprepared: Older people’s services in England in the COVID-19 pandemic 134 제3절 COVID-19 확산에 따른 노인 돌봄 환경 변화와 대응 현황 139 제4절 종합 토론 161 부록 177 〔부록 1〕 세대 연대를 위한 정책 아이디어 공모전 177

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.093
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0930.041

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.

Opus teacher head0.015
GPT teacher head0.203
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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