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Record W1967642630 · doi:10.1108/14630010310812163

Interaction, identity and collocation: What value is a corporate campus?

2003· article· en· W1967642630 on OpenAlexaff
Franklin Becker, W. Sims, Johanna Schoss

Bibliographic record

VenueJournal of Corporate Real Estate · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Identity and Reputation
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsCorporate identityCorporate communicationFlexibility (engineering)Collocation (remote sensing)BusinessSet (abstract data type)Identity (music)Value (mathematics)Corporate sustainabilityPublic relationsCorporate governanceMarketingCorporate social responsibilityEconomicsComputer sciencePolitical scienceManagementFinance

Abstract

fetched live from OpenAlex

Corporate campuses have been justified on many grounds, including lower operational costs, greater flexibility, stronger corporate branding and enhanced cross‐functional communication. Despite the tens of millions of dollars spent to acquire and develop them, little research exists that has systematically tested the validity of the benefits attributed to a corporate campus. This paper reports on an initial set of case studies examining one potential benefit of a corporate campus: the nature and extent of communication across organisational units. The results suggest that the amount of cross‐unit communication on a corporate campus may be less than expected. Implications for workplace and collocation strategies are discussed.

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.008
Scholarly communication0.0130.014
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.044
GPT teacher head0.265
Teacher spread0.221 · 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 designQualitative
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

Citations25
Published2003
Admission routes1
Has abstractyes

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