Crise demográfica em Portugal. Impactos para as Forças Armadas e Forças e Serviços de Segurança
Bibliographic record
Abstract
Em Portugal, a diminuição de nascimentos, da fecundidade e da mortalidade, a aceleração do envelhecimento, o decréscimo da população em idade ativa e o acentuar da emigração, revelam a existência de uma crise demográfica. Assim, pretende-se avaliar as consequências que, a médio e a longo prazo, esta crise terá no recrutamento e na renovação do efetivo na categoria de Praças, das Forças Armadas (FFAA), e de Guardas, da Guarda Nacional Republicana (GNR). Este estudo assenta na análise documental e em entrevistas semiestruturadas a uma amostra não-probabilística intencional, baseando-se numa estratégia de investigação qualitativa e num estudo de caso, de natureza empírica e descritiva, relacionado com estas instituições. Atento ao fenómeno, constata-se uma tendência decrescente a médio e a longo prazo da curva demográfica, com efeitos na qualidade, género e número de efetivos e candidatos, desta categoria, às FFAA. Na GNR, a crise demográfica não tem produzido efeitos no número de candidatos, excetuando no seu efetivo, em resultado de políticas orçamentais restritivas. A fim de inverter esta tendência nas FFAA, gizou-se um modelo assente num conjunto de medidas corretivas que promovam o recrutamento e a renovação do seu efetivo. Abstract: In Portugal, the decline of the births, fertility and mortality, the acceleration of aging, the decrease in the population of working age and the increase in emigration, reveal a demographic crisis. Therefore, this study pretends to evaluate the consequences, in the medium and long term, of this crisis in the recruitment and renewal in Soldiers’ category of the Armed Forces (AF) and Guards, of the National Republican Guard (NRG). This study is based on documental analysis and semi-structured interviews with an intentional non-probabilistic sample, based on a qualitative research strategy and on a case study, empirical and descriptive, related to these institutions. Considering the phenomenon, it was verified a decreasing trend in the medium and long term of the demographic curve, with effects on the quality, gender and number of personnel and candidates, of this category, for the AF. At NRG, the demographic crisis has had no effect on the number of candidates, with the exception in its staff as a result of restrictive budget policies. In order to reverse this trend in the AF, a model based on a set of corrective measures was proposed to promote the recruitment and renewal of its staff.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".