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Record W161669422 · doi:10.17705/1cais.02415

A Hybrid Tracking System of Human Resources: A Case Study in a Canadian University

2009· article· en· W161669422 on OpenAlexafffundabout
Manon G. Guillemette, Isabelle Fontaine, Claude Caron

Bibliographic record

VenueCommunications of the Association for Information Systems · 2009
Typearticle
Languageen
FieldComputer Science
TopicMobile Crowdsensing and Crowdsourcing
Canadian institutionsUniversité de Sherbrooke
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsWorkgroupReal-time locating systemGlobal Positioning SystemRadio-frequency identificationContext (archaeology)Identification (biology)Computer scienceProcess (computing)Human resourcesTracking systemService (business)Process managementBusinessComputer securityTelecommunicationsGeographyMarketingArtificial intelligence

Abstract

fetched live from OpenAlex

Radio Frequency Identification (RFID), including Real-Time Location Systems (RTLS) and Global Positioning Systems (GPS), are technologies that have evolved considerably in the past few years. They have the potential to provide a means by which organizations can follow employees in real time. However, this permanent surveillance may have unexpected impacts on employees as well as on the organization itself. We followed the systems development research process to build a hybrid RFID-GPS system that allowed for the real-time location of human resources both indoors and outdoors. We tested this system in the security service of a Canadian university and explored its impacts on the workgroup and its employees. Our findings suggest that this kind of system can work in a real-world context, and that it has distinct impacts on the individual and the organization of a type not usually observed with more traditional information systems.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.025
GPT teacher head0.254
Teacher spread0.230 · 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 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

Citations1
Published2009
Admission routes3
Has abstractyes

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