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Record W3199664326 · doi:10.3126/sirjana.v6i1.39357

SKIB-71 in Textual and Visual Memories

2019· article· en· W3199664326 on OpenAlexaboutno aff
Abhi Subedi

Bibliographic record

VenueSIRJANĀ – A Journal on Arts and Art Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionPaintingVisual artsDemiseNepaliArtSubject (documents)Quarter (Canadian coin)The artsFine artHistoryLiteratureLibrary sciencePolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

SKIB-71, the acronym of Sashi, Krishna, Indra and Batsa represents a very significant phase in the movement of modern Nepali paintings. These four artists launched a movement in paintings a quarter of a century ago with a view to inspiring young artists and opening new vista of communications with artists using other media and methods. Their personal involvements in art activities couples with their experimentations created a unique atmosphere in Nepali paintings characterised by such features as the gatherings of artists, interactions, exhibitions and, above all the pedagogy of arts. After the sad demise of Indra Pradhan in 1994, three of them continued to work for which they chose to engage in art pedagogy or to work quietly on cultural motifs. Among the many reasons to consider them as part of art activities today is their impact on art education, accentuation of the subject of experimentation and participation in the art activities of present times.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.010

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 head0.009
GPT teacher head0.279
Teacher spread0.270 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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