MétaCan
Menu
Back to cohort
Record W3164047419 · doi:10.5430/ijhe.v10n5p194

Scientific Knowledge of University Students of Chile

2021· article· en· W3164047419 on OpenAlexvenueno aff
Rubén Vidal-Espinoza, Emilio Rodríguez-Macayo, Rossana Gómez‐Campos, Rodrigo Ruay-Garcés, Sonia Isabel-Muñoz, Marco Cossio‐Bolaños

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEducational Research and Science Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct (python library)Sociology of scientific knowledgeMeaning (existential)Knowledge transferPsychologyDescriptive knowledgeMathematics educationKnowledge levelDescriptive statisticsKnowledge managementSociologyComputer scienceSocial scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Scientific research is becoming increasingly important in higher education, as it helps students to understand scientific knowledge and provides tools to construct and interpret the meaning of what science provides. This descriptive study compares the use of scientific knowledge by university according to age, entrance route and type of establishment, and verifies the possible relationships between variables. A questionnaire measuring the use of scientific knowledge (information search, knowledge transfer and knowledge contribution) was administered to 187 university students. The results showed that there were no significant differences in the use of scientific knowledge by indicator and in the total scores among the three universities. A positive correlation was observed between age with knowledge contribution and type of school and knowledge transfer with type of school. It is concluded that the type of school could be relevant to obtain better results in the contribution and transfer of scientific knowledge, although age could also contribute.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.362
Teacher spread0.326 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicEducational Research and Science TeachingFrench-language works237,207