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Record W7128493867 · doi:10.64903/1480-6800-26.3-4.358

Energy Management Supported by Various Actors in Residential Rehabilitation Projects: Example of Building Rehabilitation on Tripoli Street, Municipality of Hussein-Dey, Algiers, Algeria

2023· article· W7128493867 on OpenAlexvenueno aff
Manal Amoura-Tennoun, Maha Messaoudène

Bibliographic record

VenueArab world geographer · 2023
Typearticle
Language
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy managementPoliticsEnergy policyEnergy (signal processing)RehabilitationEfficient energy useField (mathematics)

Abstract

fetched live from OpenAlex

Energy management is a global issue that has piqued Algeria’s interest since the late 1990s, prompting the country to strengthen its legal framework to promote the integration of energy efficiency. However, the energy efficiency program, designated a national priority, is difficult to implement. This article examines how institutional, technical, and social actors manage residential energy efficiency in a residential rehabilitation project on Tripoli Street in the Algiers municipality of Hussein-Dey. The goal is to comprehend each actor’s role and its true significance in energy management. Furthermore, it allows us to contrast the reality on the ground, as constructed by technical actors and experienced by residents, with the ambitious political discourse that reflects a clear desire for this program. The analysis of technical and regulatory documents and the field survey combining interviews and questionnaires revealed that energy management could not be achieved without the genuine participation of all stakeholders, particularly residents who are hesitant to adhere to and finance the energy aspects of their house renovations. It demonstrates the importance of supplementing Algeria’s energy management policy with regulatory, financial, and communication measures.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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.

Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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