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Record W3158645273 · doi:10.35429/jcp.2020.11.4.1.8

Liderazgo complejo como elemento para mejorar el índice de aprobación

2020· article· en· W3158645273 on OpenAlexaboutno aff
Claudia Rocío Tovar-Rosas, Luis Roberto Garza-Moya, Josué Mizraim Arreola-Burciaga, Francisco de Borja Rodríguez Alanis

Bibliographic record

VenueRevista de Pedagogía Critica · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsnot available
Fundersnot available
KeywordsTeamworkSubject (documents)ExploitWork (physics)Quarter (Canadian coin)Knowledge managementHumanitiesComputer scienceManagementLibrary scienceEngineeringGeographyArt

Abstract

fetched live from OpenAlex

We currently live in a globalized world, which requires having certain knowledge and skills in order to carry out daily work activities, such as knowledge of some programming language, databases, among others. Many of the companies currently request universities that graduates not only have the necessary knowledge to carry out activities, but rather that graduates have the skills of being, to display their knowledge as it is, teamwork, leadership, the development of work schedules, among others. For all of the above, a way was sought to impart not only the essential knowledge of a subject in the classroom, but also a way to exploit the abilities of each student within the classroom, which is why complex leadership was implemented. at the “Universidad Politécnica of Gómez Palacio”, specifically in the 6th quarter grade of the Information Technology degree, implemented in the database subject, with this, a strategy was sought to improve the approval rate, since this specific subject is a difficult subject for the students of the degree to understand.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.182
GPT teacher head0.452
Teacher spread0.270 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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