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Record W2915268912 · doi:10.5539/res.v11n1p183

How the Widespread Presence of Historical Private Real Estate Can Contribute to Local Development

2019· article· en· W2915268912 on OpenAlexvenueno aff
Luciano Monti, Roberto Cerroni

Bibliographic record

VenueReview of European Studies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateCommodificationEstateLimitingCultural heritageBusinessReal estate developmentEconomicsEconomyFinancePolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

For decades, historical assets have been considered, particularly real estate, as a heritage to be conserved, but limiting the use to museums. The concept of enterprise was considered far removed, if not an indication of the dangerous commodification of the aforementioned assets. On the one hand, the emergence of an ever-increasing demand for cultural services connected to this patrimony, and, on the other hand, the increasing difficulties in finding adequate resources for the conservation of the latter, have pushed a greater number of operators to take into consideration the instrument of cultural industry, the latter whose goal is to secure resources for the maintenance of the artistic historical patrimony by exploiting the potential of the same. Italy is an important test for this challenge, because it can count on an intense pool of historical and artistic heritage, that is unique and unrivalled in the world. In this paper, therefore, we try to relate the investments necessary for the conservation and enhancement of the Italian private historical real estate assets, with the concentration of the aforementioned in certain realities and with the current local economic development of cultural and creative industries. The cross analysis shows clearly how the enhancement of private real estate assets is particularly relevant in smaller cities and can represent a stimulus for a specific economic, social and cultural growth model. However, this opportunity at the local level is unfortunately not always cultivated, therefore, we call for a comprehensive set of structural, long-term interventions in the sector, both at national and supranational level, for not only economic but social revival of private historical heritage.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.237

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 head0.055
GPT teacher head0.281
Teacher spread0.226 · 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 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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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