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Record W4226397622 · doi:10.5430/wjel.v12n3p55

A Study on Creativity, Innovation, and Knowledge

2022· article· en· W4226397622 on OpenAlexvenueno aff
Onkar Bagaria, Gautam Kumar, M. Sharma, Ravinder Saini

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityKnowledge managementProcess (computing)The artsCreativity techniqueComputer scienceDomain (mathematical analysis)Engineering ethicsSociologyEpistemologyPsychologyPolitical scienceSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Creativity or innovation address the process of developing and applying new information. As a consequence, they are vital to Organizational Learning. However, as organizational learning is a new career, innovation or creativity must be addressed in this new setting. This research starts by proposing a framework inside which these difficulties may be examined. It then continues on to look at just how human creativity is inhibited in numerous ways, particularly deep-seated views of the world. Finally, this research evaluated two devices that promote knowledge management or creativity: discussion in the human domain as well as groupware in the technical area. The authors of this research investigate the notions of creativity, innovation, or knowledge, but also their responsibilities. Innovation and uniqueness are required in all academic areas or educational activities, not simply the arts. People can think critically, tackle complicated problems, but also come up with innovative solutions if they have creativity. Creativity allows people to think critically, tackle complicated problems, and come up with unique solutions. People are robust and adaptable, whether they're creative, see things in new ways, or are prepared to learn and understand as they go. This study will assist people in broadening their understanding of creativity and innovation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.336
Teacher spread0.302 · 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 designQualitative
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

Citations2
Published2022
Admission routes1
Has abstractyes

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