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Record W7103421388

Digitalization on Macroeconomics learning by FRED

2023· article· es· W7103421388 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRepositorio Institucional Universidad Católica de Colombia (Universidad Católica de Colombia) · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Quarter (Canadian coin)Work (physics)Economic modelPublic policyEducation economicsEconomic data
DOInot available

Abstract

fetched live from OpenAlex

Estudio de innovación docente y digitalización en Macroeconomía vía plataformas en línea y con datos reales. Dada la desafección de los estudiantes con los modelos ortodoxos macroeconómicos y sus métodos docentes poco atractivos, se ofrece aquí una alternativa de síntesis heterodoxa, basada en un proyecto de innovación docente que se viene desarrollando en la Universidad Rey Juan Carlos. Los alumnos, en clases invertidas y gamificadas, han tenido acceso continuo a prácticas, datos y gráficas de FRED ® (Federal Reserve Economic Data), del Banco de la Reserva Federal de St. Louis. Los resultados han sido muy positivos, por lo que se ha ampliado a otros recursos en línea.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0050.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.310
Teacher spread0.294 · 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