MétaCan
Menu
Back to cohort
Record W4392198800 · doi:10.31428/10317/9859

Retos de la industria 4.0 desde la perspectiva del graduado social

2024· article· es· W4392198800 on OpenAlexaff
Ruiz la Rosa Javier

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldSocial Sciences
TopicTechnology in Education and Healthcare
Canadian institutionsCentre Intégré de Santé et de Services Sociaux des Laurentides
Fundersnot available
KeywordsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Desde el punto de vista del Colegio de Graduados Sociales de Murcia entendemos que los logros de la profesión de Graduado Social tienen no solamente un componente legal sino efectivamente tecnológico toda vez que desde el inicio de la profesión siempre se ha ido de la mano de las mejoras informáticas y tecnológicas que se iban sucediendo en la administración y en el seno de los propios despachos. Herramientas tales como el sistema de declaración de accidentes DELTA, el sistema RED, sistema Creta, y otros tantos sistemas de comunicación para una mejor funcionalidad y aplicación de las obligaciones y derechos de la normativa laboral española se han convertido en funcionalidades imprescindibles en el día a día del trabajo de los despachos generando que en el profesional graduado social se aúnan en muchas ocasiones capacidades que si bien no parecen habituales a priori se han convertido en una realidad, como por ejemplo el conocimiento del lenguaje de programación XML que es el que protagoniza los ficheros de comunicación con las distintas administraciones y que a lo largo de los últimos años ha permitido que los colegiados se conviertan de alguna manera en conocedores de esta tecnología.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0120.007
Open science0.0030.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.010

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 head0.037
GPT teacher head0.444
Teacher spread0.407 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicTechnology in Education and HealthcareFrench-language works237,207