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Record W4392378178 · doi:10.31428/10317/11906

El uso de SIG de software libre para la consolidación de contenidos de la Geografía Física de España en 2º de bachillerato

2024· article· es· W4392378178 on OpenAlexaff
Martínez Hernández Carlos

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldSocial Sciences
TopicGeography and Education Methods
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsComputer scienceHumanitiesArt

Abstract

fetched live from OpenAlex

[SPA] El objetivo de este estudio es conocer la percepción que tienen los estudiantes del Grado en Trabajo social sobre el concepto de competencia y sobre las competencias adquiridas a través de su formación, para proponer alternativas o instaurar mejoras, de cara a disminuir los posibles efectos negativos de la reciente implantación de los nuevos títulos y ofrecer una formación de la máxima calidad. Para llevar a cabo la investigación y conocer la opinión de los agentes directos que forman parte del sistema universitario, se elaboró un cuestionario estructurado que se puso a disposición de todos los alumnos. Dicho cuestionario fue cumplimentado por un total de 170 estudiantes. Se procedió a la explotación de datos para la obtención de resultados. Los estudiantes desconocen el concepto de competencia, así como la diferenciación entre su clasificación. En cuanto a la evaluación de la adquisición de las competencias específicas, los resultados no muestran una evaluación negativa. [ENG] The aim of this study is to know the perception that the students of the Degree in Social Work has on the concept of competence and on the competences acquired through their formation, with a view to proposing alternatives or introducing improvements, in order to reduce the potential negative effects of the recent implantation of the new degrees and offer training of the highest quality. To carry out this investigation and to know the opinion of the direct agents who form part of the university system, a structured questionnaire was elaborated. It was put at the disposal of all the pupils. This questionnaire was fulfilled by 170 students. We proceeded to the exploitation of data to obtain results. Students do not know the concept of competence or the differentiation between its classification. Regarding the assessment of the acquisition of specific competences, the results do not throw a negative evaluation.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.391
Teacher spread0.374 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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Same topicGeography and Education MethodsFrench-language works237,207