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Record W4281773147 · doi:10.3390/bs12060182

Multiple Identifications of Employees in an Organization: Salience and Relationships of Foci and Dimensions

2022· article· en· W4281773147 on OpenAlexaff
Andrey V. Sidorenkov, Eugene Borokhovski, Wladimir Stroh, Elena Aleksandrovna Naumtseva

Bibliographic record

VenueBehavioral Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia University
FundersRussian Foundation for Basic Research
KeywordsSalience (neuroscience)PsychologySocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

This research addresses: (1) the salience of employees' social (organizational, sub-organizational, group, micro-group), interpersonal, and personal identifications and their dimensions (cognitive and affective); (2) and the relationship and structure of the identifications of employees in different areas of professional activity. The study was conducted on independent samples of employees in the socio-economic sphere (241 participants), in the law enforcement agency (265), and in higher education (172). To assess the respective identification foci and dimensions, the study employed four questionnaires. The personal identification was the weakest and the micro-group identification was the strongest for both dimensions in all samples. The affective dimension prevails over the cognitive in all identifications, except for interpersonal. Social identifications were significantly positively correlated to each other in all samples whereas personal identification was significantly negatively correlated with all social identifications (on the affective dimension) in two samples. The results expand our understanding of the identifications of employees in organizations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.270
GPT teacher head0.423
Teacher spread0.153 · 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 designObservational
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

Citations8
Published2022
Admission routes1
Has abstractyes

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