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Record W2888603393 · doi:10.31372/20180302.1081

Features of Women’s Leadership and Nursing in Japanese Culture

2018· article· en· W2888603393 on OpenAlexvenueno aff
Reiko Sakashita

Bibliographic record

VenueAsian/Pacific Island Nursing Journal · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsFollowershipPoliticsServant leadershipHuman resourcesHappinessSociologyPublic relationsTransactional leadershipPolitical scienceManagementLaw

Abstract

fetched live from OpenAlex

Mutual respect and harmonious relationships between people can be viewed as the fundamental spirit of Japanese culture. Japanese leaders try to keep the peace in human relations, rather than to achieve a goal. They become leaders due to internal and external conditions, rather than their own abilities. Japanese society has been basically male-dominated though the ratio of women leaders has increased recently. Higher education and development of academic societies in nursing have been very successful during the last two decades and nursing is one of the most trusted professions in Japan. However, some Japanese people still think a nurse is just an assistant to doctors. The purpose of this paper is to clarify features of women's leadership in Japan from the nursing point of view. In Japanese culture, leaders need to possess human resource leadership as an indispensable element, while good leaders may have advantages in all four frames of leadership including structural, human resource, political, and symbolic frames. Japanese organizations are also supported by excellent followership which might be influenced by the structure of Japanese society which is called "the vertical society" where leaders are fostered from among followers. What is important in Japanese leadership is to have a vision that leadership leads to the happiness of all people in a shareable form.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.305
Teacher spread0.282 · 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 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

Citations10
Published2018
Admission routes1
Has abstractyes

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