MétaCan
Menu
Back to cohort
Record W3019866965 · doi:10.5430/ijhe.v9n3p269

Interdisciplinary Instruction: Between Art and Literature

2020· article· en· W3019866965 on OpenAlexvenueno aff
Nitza Davidovitch, Ruth Dorot

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsMultidisciplinary approachPerspective (graphical)CurriculumPoetryHigher educationValue (mathematics)Engineering ethicsDisciplineSociologyNorwegianPaintingPedagogyMathematics educationVisual artsPsychologyArtComputer scienceSocial scienceEngineeringPolitical scienceLiteraturePhilosophyLinguistics

Abstract

fetched live from OpenAlex

This paper explores the developments and trends in higher education from a pedagogical perspective (specifically, multidisciplinary curricula) and research perspective (the interdisciplinary approach), and traces them from a last resort option to their recognition as a legitimate development with added value. The paper focuses on a case study that integrates two disciplines, art and literature, based on the poem by the Israeli poet Rachel entitled My Book of Poems and the painting The Scream by Norwegian artist Eduard Munch. The interdisciplinary approach opens up possibilities of enriching, expanding horizons, and breaking boundaries, and can grant graduates of the higher education system a cultural perspective suitable for the current generation of students, who typically use multiple interactive media and platforms, often simultaneously. This paper may shed light on teaching and learning of many diverse fields. The case study illustrates the joy of interdisciplinary learning and its academic benefits, despite the fact that for years, higher education institutions have tended to refer to researchers’ specializations in specific academic disciplines. This case study may serve as a model or source of inspiration for multidisciplinary learning involving motifs and topics that traditionally represent specific disciplines.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.445
Teacher spread0.387 · 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.

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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicInterdisciplinary Research and CollaborationFrench-language works237,207