MétaCan
Menu
Back to cohort
Record W3011825678 · doi:10.34069/ai/2020.26.02.12

Impact of Intangible Characteristics of Universities on Student Satisfaction

2020· article· en· W3011825678 on OpenAlex
Noor Us Sabbah Khan, Yunus Yıldız

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

VenueRevista Amazonia Investiga · 2020
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsImpact
Fundersnot available
KeywordsReputationAtmosphere (unit)Test (biology)Public relationsEnvironmentally friendlyMarketingPsychologyBusinessPolitical scienceSociologySocial scienceGeographyMeteorology

Abstract

fetched live from OpenAlex

The aim of this research was to investigate the impact of the intangible qualities of the universities on student satisfaction. To do this, we have collected data from 7 different major public and private universities of the Kurdistan Region of Iraq. We have used 170 data to proposed further analysis. The partial least square method (PLS) was used to test the hypothesis. The results reveal that career opportunities and a friendly atmosphere are the main two elements that foster the reputation of the universities. The second interesting result of this research is that social activities impact the reputation of universities but not the friendly atmosphere while social activities impact a friendly atmosphere but not the reputation significantly. Finally, we have suggested the implications to the practitioners in the region.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.059
GPT teacher head0.364
Teacher spread0.305 · 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