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Record W2592966865 · doi:10.1111/1911-3838.12135

Collaborative Resource Solutions

2017· article· en· W2592966865 on OpenAlexaffvenue
Nathalie Johnstone, Vince Bruni‐Bossio

Bibliographic record

VenueAccounting Perspectives · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRubricPurchasingValuation (finance)Resource (disambiguation)Order (exchange)Knowledge managementKey (lock)Computer scienceBusinessProcess managementAccountingMarketingFinance

Abstract

fetched live from OpenAlex

Abstract Collaborative Resources Solutions ( CRS ) is based on a real situation outlining the issues related to buying a service organization. This instructional case requires students to provide advice to a client who is considering purchasing a 50 percent ownership of a similar consulting business with the vision of blending the two companies together and increasing the knowledge base of her current company; therefore improving the ability to target more to clients. The case requires the students to evaluate the strategic, valuation, and financial issues in considering the acquisition of the existing business. In order to do so, students are required to analyze the financial information provided, both historical and forecasted, as well as analyzing key internal operations issues that may impact the future success of the business. This case is suitable for use in upper‐level undergraduate business strategy courses and accounting courses, as well as in master‐level accounting courses. Assessment rubrics and teaching notes accompany the case for use by instructors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.178
Threshold uncertainty score0.595

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0080.006
Open science0.0040.009
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.1780.063

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 head0.034
GPT teacher head0.310
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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