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Record W4289078054 · doi:10.5430/bmr.v11n1p1

Humility as Self-Discovery–Leadership Insights for Human Resource Professionals

2022· article· en· W4289078054 on OpenAlexvenueno aff
Michael Kirchner, Cam Caldwell

Bibliographic record

VenueBusiness and Management Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsnot available
Fundersnot available
KeywordsHumilityInterpersonal communicationPsychologyInterpersonal relationshipResource (disambiguation)Engineering ethicsSocial psychologyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Purpose: The purpose of this paper is to help Human Resource (HR) Professionals understand six elements of self-discovery and to apply those elements in understanding the relationship of self-discovery to humility.Design: This summary is prepared by independent writers who specialize in the development of interpersonal leadership skills and includes their insights about the importance of self-discovery and humility in building interpersonal relationships.Findings: This paper explores how six elements of self-discovery can apply in understanding the three pillars of humility and in incorporating humility in building interpersonal relationships for HR professionals. Based upon the literature about humility and leadership, the utilization of a self-development process can inform HR professional's approach toward strengthening interpersonal relationships.Originality: This briefing offers HR professionals insight into how application of the six elements of self-discovery can contribute toward their effectiveness as leaders by developing greater humility in their approach to interpersonal relationships.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.010
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.220
GPT teacher head0.439
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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