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Record W4300046052 · doi:10.54144/ijis.v2i2.38

China dan Politik Penanganan Pandemi Covid-19

2021· article· id· W4300046052 on OpenAlexaff
Arif Wicaksa

Bibliographic record

VenueInterdependence Journal of International Studies · 2021
Typearticle
Languageid
FieldSocial Sciences
TopicCOVID-19 Prevention and Impact
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceCoronavirus disease 2019 (COVID-19)ChinaHumanities2019-20 coronavirus outbreakLawMedicinePhilosophyVirology

Abstract

fetched live from OpenAlex

China merupakan Negara pertama yang dilaporkan menjadi tempat berkembangnyaCoronavirus atau Covid-19 yang saat ini menjadi pandemic global. Sebelum itu, China telahdikenal pula sebagai Negara yang bangkit sebagai kekuatan ekonomi baru dunia. Denganmenggunakan konsep Kapitalisasi Bencana dan analisis kualitatif, tulisan ini berusahamenggambarkan kepentingan China dari kebijakan-kebijakan yang diambil terkait PandemiCovid-19. Ditemukan bahwa kebijakan China terkait Pandemi Covid-19 merupakan instrumenyang digunakan untuk mencapai kepentingan nasionalnya. Kebijakan menutupi pemberitaanmedia terkait Covid-19, kemudian kebijakan memberikan bantuan kemanusiaan kepadaNegara mitra dagang China hingga kebijakan untuk penguasaan kembali Hong Kong ditengahPandemi Covid-19

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.087
GPT teacher head0.454
Teacher spread0.367 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2021
Admission routes1
Has abstractyes

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