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Record W3007872568 · doi:10.31857/s268667300008244-8

Main Tendencies in Reforming Human Services within the U.S. Defense Enterprise at the 21st Century Beginning

2020· article· en· W3007872568 on OpenAlexaff
Igor Prokopiev

Bibliographic record

VenueUSA & Canada Economics – Politics – Culture · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsHuman servicesBusinessSocial securityInvestment (military)Military personnelAdministration (probate law)Service (business)Public relationsRemunerationPublic administrationPolitical scienceFinanceEconomicsPoliticsEconomic growthLawMarketing

Abstract

fetched live from OpenAlex

Providing for social services in the Armed Forces is one of the pillars of maintaining U.S. national defense and security, keeping military service attractive. Social benefits for personnel and their families are aimed at increasing efficiency of the defense enterprise, achieving sufficient level of moral, discipline and readiness. Managing military social services is an important, yet a complicated task. U.S. military leadership considers reforming this area of responsibility one of its top priorities on the list of changes within the Department of Defense that has been steadily increasing investment in human capital since the beginning of the 21st century. As of today, social expenditures comprise approximately 30% of the military budget – almost twice as the expenditures on procuring weapons and military equipment. However, budgeting per se does not mean automatic boost in efficiency of the provided social services. Therefore, the investment is directed at establishing and strengthening a viable management system that allows to administer at the best value the resources at disposal. Since 1990s, the Pentagon has been systematically reorganizing the military healthcare, modernizing remuneration mechanisms and social security of the service members, enhancing retirement system, etc. Major achievements were registered during the administrations of George W. Bush and Barack Obama. In 2010s, the defense enterprise faced the effects of the protracted armed conflicts and austere economic environment that required addressing a wide range of issues related to recovery and social reintegration of the veterans. Donald trump administration in general is committed to the health care and retirement agenda set forth by its predecessors. However, it has yet to resolve all the difficulties due to the inherent red tape that accompanies these are as of activity. Nevertheless, it appears the Americans will continue reforming the military social services in the foreseeable future.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.206
Teacher spread0.185 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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