Strategic Management and Retention of Talent: Challenges in the Portuguese Army
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Organizations are made up of people, their most important asset. The Armed Forces are no exception in this context, quite the opposite. Despite all the developments in military equipment, especially in the last century, the human component continues to be a determining factor in the overwhelming majority of the weapons systems. The investments that have been made to the military, in terms of academic, technical and operational training, have contributed to increasing their skills and abilities in a professional career that, today, is facing even more asymmetrical challenges. Different levels of motivation, different career aspirations linked to organizational constraints and different economic contexts, have led to an increasingly difficult strategic management of human resources in the military areas, such as the Portuguese Army. This article addresses the urgency of retaining talent in the Portuguese Army, at a time when this branch of the Portuguese Armed Forces is confronted with new assignments, missions and challenges.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it