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Record W4404427017 · doi:10.61438/sarj.v1i1.62

Introducing Salamat Academic & Research Journal

2024· article· en· W4404427017 on OpenAlexaff
Nasar Ahmad Shayan, Musa Joya

Bibliographic record

Venueسلامت. · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

We are thrilled to announce the launch of the Salamat Academic & Research Journal (SARJ), an open-access publication of Jami University dedicated to advancing medical and health sciences. SARJ, licensed by Afghanistan's Ministry of Higher Education in 2024, provides a prestigious, accessible forum for the publication of high-quality, peer-reviewed research. This journal is the latest initiative in Afghanistan's storied commitment to medical education, which began with the establishment of the Faculty of Medicine in Kabul in 1932 [1]. SARJ extends this legacy, offering Afghan and global researchers a platform for innovation in medical sciences. The establishment of SARJ also builds on the foundation of scholarly communication in Afghanistan. The Afghan Medical Journal (AMJ), initiated in 1956 by Kabul University, pioneered medical publication in Afghanistan, creating one of the nation’s first platforms for sharing scientific findings [2]. AMJ, originally published from 1956 to 1963, laid the foundation for scholarly medical publishing in Afghanistan and has recently been revitalized to continue its mission. The newly relaunched AMJ is now an official national and international open-access peer-reviewed journal. Initiatives like SARJ proudly follow in AMJ's footsteps, contributing to a robust and accessible platform for advancing medical research and public health in Afghanistan [3]. SARJ honors this tradition by fostering an inclusive academic community that makes quality medical research available to all, from students and researchers to healthcare practitioners.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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 score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.117
GPT teacher head0.444
Teacher spread0.328 · 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
Published2024
Admission routes1
Has abstractyes

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