MétaCan
Menu
Back to cohort
Record W2032626954 · doi:10.1506/ap.7.4.4

Designing and Implementing an Information System for the Dental Office of Branckowitz & Young

2008· article· en· W2032626954 on OpenAlexaffvenue
Alex Nikitkov, Barbara Sainty

Bibliographic record

VenueAccounting Perspectives · 2008
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsBrock University
Fundersnot available
KeywordsFlexibility (engineering)Scope (computer science)Computer scienceService (business)Context (archaeology)Class (philosophy)Resource (disambiguation)Order (exchange)Knowledge managementProcess managementEngineering managementBusinessEngineeringMarketingManagement

Abstract

fetched live from OpenAlex

ABSTRACT This case provides students with the opportunity to create a functional information system (IS) for a service company. The case facilitates a guided hands‐on experience where students learn to analyze a business entity in the context of its environment; recognize what business processes comprise an entity's value chain; and develop, document, and implement a tailor‐made IS to support the entity's operation. In order to keep the amount of development realistic and the system transparent for students, the case focuses on a small service company: a dental office. The case uses a resource—events—agents (REA) analytical framework for modeling and Microsoft Access for IS implementation. The case is structured modularly, enabling instructors to either explain material or demonstrate analysis/development of a segment of an IS in class and then challenge the students to complete the module's development following the instructor's example. Instructors have the flexibility to give students fewer (or additional) directions in developing the information system, depending on the students' backgrounds and abilities. Instructors also have a choice to limit the scope of the development and implementation to any number of four business processes.

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 score0.354
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.026
GPT teacher head0.265
Teacher spread0.239 · 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

Citations1
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueAccounting PerspectivesSame topicSemantic Web and OntologiesFrench-language works237,207