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Record W7047271800

A formação conjunta no âmbito das informações militares

2021· other· pt· W7047271800 on OpenAlexfundno aff

Bibliographic record

VenueRepositório Comum (Repositório Científico de Acesso Aberto de Portugal) · 2021
Typeother
Languagept
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersUniversidade do PortoU.S. Air ForceEgg Farmers of CanadaU.S. Department of Defense
KeywordsContext (archaeology)PortugueseAcademic communityProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Decorrente do atual contexto internacional, as informações militares enfrentam um grande desafio, que se traduz na habilitação dos seus recursos humanos, carentes de especialização nesta vertente. Com o presente trabalho pretende-se analisar o modelo formativo de informações militares, em uso nas Forças Armadas Portuguesas e verificar de que forma poderá ser otimizado. A metodologia de investigação seguiu a lógica de um raciocínio indutivo, assente numa estratégia qualitativa, baseando-se num desenho de pesquisa de estudo de caso, através de uma análise documental e de conteúdo, com recurso a entrevistas semiestruturadas efetuadas a especialistas em informações militares. Identificaram-se as lacunas patentes no presente modelo formativo, tendo por base as competências inerentes ao desempenho de funções de informações militares, bem como os exemplos de outros modelos formativos praticados por países amigos, são apresentadas propostas para a edificação de um modelo formativo de cariz conjunto, no seio das Forças Armadas Portuguesas. Concluiu-se que o modelo formativo atual se encontra descentralizado, sofrível de coordenação e complementaridade, sendo possível otimizá-lo através de uma reformulação assente em recursos e mecanismos já existentes, incrementando novos. Contribuindo assim, para um salto qualitativo, no sentido de garantir a harmonização do conhecimento e permitir o reconhecimento geral de uma competência específica. Abstract: Due to the current international context, Intelligence faces a great challenge, which translates into the qualification of its human resources, lacking in specialization. The intend of this work, is to analyze the Intelligence education model, in use in the Portuguese Armed Forces and to verify how it can be optimized. The research methodology followed the logic of an inductive reasoning, based on a qualitative strategy and a case study research design, through a documentary and content analysis, using semi-structured interviews with Intelligence specialists. The gaps in the national education model were identified, and based on the competencies inherent to the performance of Intelligence job descriptions, as well as examples of other friend countries education models, proposals are presented for the building of a joint education model within the Portuguese Armed Forces. It was concluded that the current education model is decentralized, suffering from coordination and complementarity, being possible to optimize it through a reformulation based on existing resources and mechanisms, increasing others, thus contributing to a qualitative leap, in order to ensure the harmonization of knowledge and allow the general recognition of a specific competence.

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)0.002
metaresearch head score (Gemma)0.000
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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2050.002

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 head0.011
GPT teacher head0.280
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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