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Record W3004649568 · doi:10.5430/bmr.v8n4p54

New Means of Workplace Surveillance Model: From the Gaze of the Supervisor to the Digitalization of Employee Performance

2020· article· en· W3004649568 on OpenAlexvenueno aff
Hanen Khanchel

Bibliographic record

VenueBusiness and Management Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsSupervisorGazeControl (management)Data collectionOrder (exchange)BusinessComputer scienceApplied psychologyOperations managementPsychologyManagementEngineeringEconomicsArtificial intelligenceFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

Based on a data collection using the puzzle method. This method allowed us, first, to collect new types of data on Tunisian workers, in order to better quantify and analyse their activities. As a result, this study shows the consequences of the excessive use of employee behaviour monitoring tools and control devices established in Tunisian companies after the events of 14 January 2011. Indeed, it has been found that the system of control and monitoring of employee behaviors can feed the sources of stress and burn-out. Finally, some recommendations were proposed to address these issues.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.093
GPT teacher head0.383
Teacher spread0.289 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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