MétaCan
Menu
Back to cohort
Record W2946684724 · doi:10.26791/sarkiat.547257

MOTÎFA ŞERTDANÎNÊ DI KILAMÊN DENGÊJAN DE

2019· article· ku· W2946684724 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuee-Şarkiyat İlmi Araştırmaları Dergisi/Journal of Oriental Scientific Research (JOSR) · 2019
Typearticle
Languageku
FieldArts and Humanities
TopicLinguistics and Cultural Studies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Kurte Di nav çanda devki ya kurdan de dengbêjî xwediyê cihekî girîng e. Di van kilaman de evîn û şerên mezin, pirsgirêkên siyasî, civakî û hwd. hatine vegotin. Ew kilam û destanên ku bi hunera dengbêjan bûne bîra civakê, xwediyê gelek taybetiyan e. Yek ji van taybetiyên kilam û destanên kurdî, danîna şertan e. Ango kilam li ser hîmê şertan ava dibe. Bi taybetî em di kilamên dengbêjan yên şer û evîniyê de danîna gelek şertan dibînin. Ji bo xurtkirina naveroka destanê, ceribandina lehengan, berjewendiyên takekesî, ji bo nedana keçikê û hwd. şertên giran li pêşiya leheng têne danîn. Şert, piranî ji aliyê keçik an jî bavê keçikê ve têne danîn. Kesê ku şertan bîne cih jî gelek caran dilketî ye. Hewldanên dilketî, ji aliyekî ve di nav guhdaran de rê li ber kelecanê vedike ji aliyê din ve naveroka kilamê dewlemend dike. Di vê gotarê de mîjara me, di kilamên kurdî yên dengbêjan de danîna hin şertan e. Di vê gotarê de dê hewl bê dayîn da ku rewşa danîna şertan, sedemên wan û encamên wan were.

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.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0060.002
Open science0.0030.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.065
GPT teacher head0.337
Teacher spread0.271 · 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