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Record W1988459449 · doi:10.1108/14630010410812405

A case study for in‐house real estate services

2004· article· en· W1988459449 on OpenAlex
Guy Knowles

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Corporate Real Estate · 2004
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsRogers Communications (Canada)
Fundersnot available
KeywordsReal estateCorporate Real EstateReal estate investment trustBusinessCentralisationReal estate developmentOutsourcingService providerService (business)FinanceMarketingEconomicsMarket economy

Abstract

fetched live from OpenAlex

Many corporations in recent years have considered outsourcing administrative or non‐essential functions. This reflects both a corporate desire to focus on core functions and growing sophistication among providers of these secondary functions. Rogers Communications Inc., one of Canada’s largest, diversified telecommunications enterprises, considered this option but chose instead to retain and centralise its extensive real estate interests into a single corporate real estate function. The following case study traces Rogers’ experience, from the starting point of distinct real estate portfolios managed by individual operating groups to the end point of a centralised, full‐service, corporate real estate function. It discusses various challenges faced by Rogers during the centralisation process and the corresponding measures and strategies that contributed to a successful outcome. It also suggests useful performance indicators to measure the value contributed by the corporate real estate function.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.990

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.053
GPT teacher head0.323
Teacher spread0.270 · 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