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Record W4231800818 · doi:10.33423/jabe.v22i9.3665

Climate Applications

2020· article· en· W4231800818 on OpenAlexvenueno aff
Denis Rudd, Richard J. Mills

Bibliographic record

VenueJournal of Applied Business and Economics · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsnot available
Fundersnot available
KeywordsOrganisation climateTeamworkFunction (biology)Atmosphere (unit)Competition (biology)Organizational cultureIdeologyPublic relationsBusinessSociologyKnowledge managementMarketingPolitical scienceComputer scienceManagementEcologyEconomicsGeographyPoliticsMeteorology

Abstract

fetched live from OpenAlex

The climate of the organization is crucial in creating an effective organization. Climate is a part of the internal environment of the organization. Some people feel that climate results from physical layout of the company. The interior decor, the color-coordinated furnishings, the uniforms worn by the workers or the size of the offices may create a certain ambiance. Although such factors can influence workplace atmosphere, we have to define climate in a broader sense. The fabric of the organization that enables it to function and explain why it acts the way it does is called organizational culture or climate. It is defined as "the philosophies, ideologies, values, assumptions, beliefs, expectations, attitudes, and norms that knit an organization together and are shared by employees." All these behavioral concepts form an organization's consensus, implicit and explicit, on how to approach decisions and problems in the organization. In other words, climate provides a framework that explains "the ways things are done around here." There are numerous examples of organizational culture and its impact on organizations. Delta Airlines stresses teamwork among employees, Hewlett-Packard believes in entrepreneurship and PepsiCo wants aggressive managerial behavior and competition both within and outside the organization.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.183
Teacher spread0.167 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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