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Record W4310259056 · doi:10.1177/03063070221131980

On the economic nature of behavioural control in smart real estate

2022· article· en· W4310259056 on OpenAlex

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.

Bibliographic record

VenueJournal of General Management · 2022
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsReal estateCyberneticsControl (management)Space (punctuation)Process (computing)Property (philosophy)EstateSmart environmentArchitectural engineeringComputer scienceBusinessComputer securityEngineeringArtificial intelligenceInternet of ThingsEpistemology

Abstract

fetched live from OpenAlex

The implementation of smart technologies in the built environment presents unprecedented opportunities and challenges for the real estate sector. Among the challenges is building occupants’ behavioural control due to smart buildings’ technological apparatus underpinned by pervasive computing. Since the early days of cybernetics, control stemming from information technology has generated many arguments about freedom, privacy and surveillance. Arguments only focused on technology or ethics tend to foster a Manichean view which obscures our ability to rationally assess calm and transparent technology’s role in controlling space users’ behaviours in smart buildings. The paper applies two classic economic frameworks to decipher the economic nature of behavioural control in smart real estate. In the process, it sheds some light on the complex utilitarian relationship between behavioural control and smart space’s user centricity. It concludes by assessing whether regulators should step in, for instance, through de jure property rights allocation among all parties.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.166

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 science0.0010.000
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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