Bibliographic record
Abstract
Sustainable and resilient extractive and circular economies under uncertainty" Increased uncertainty in the global economy and business environment characterised by rapid demand fluctuations and technological breakthroughs, digitization and global disruptions, creates the need for systemic transformation of extractive and construction industries.Extractive industries such as the mining and oil and gas (O&G) sectors, and the construction industry that includes both construction and demolition (C&D) sectors have faced systemic transformative changes during recent decades.These industries now respond to increasing calls for embracing sustainability and resilience practices by dealing with the unique complexity of mining, O&G and C&D projects and by developing resilient and digitally integrated value chains particularly with recent developments of the Industry 4.0.In addition, the recent COVID-19 pandemic has demonstrated the vulnerabilities of globally connected supply chains.This has created a pressing need to develop novel organisational strategies and policy frameworks to ensure that business operations are resilient to unforeseen disruptions and to secure uninterrupted service and supply of (critical) materials and products.Prior research has been concerned with the application of emergent technologies to improve effectiveness and sustainability of project delivery systems, and the logistics of extractive operations and construction sites.The extant research has also discussed various facets of circular economies, closed-loop supply chains and reverse logistics solutions, supply chain digital transformation and resilience.Recent global supply disruptions, which will possibly become a norm in the future, calls for novel managerial approaches, policy and regulatory frameworks, decision-support tools and technological innovations that collectively enable a critical step towards sustainable and resilient extractive and construction industries and enhance value creation for all industry stakeholders.The special issue addresses this challenge by exploring how extractive and C&D sectors manage the emerging trends and prepare to face the uncertainty through changing management paradigms and technological innovations.It aims to facilitate discussion and attract relevant research to extend the boundaries of project management and decision-making theory, policy and practice as applied to the extractive and construction sectors.Four papers of this special issue focus on the challenges faced by the C&D sector.For example, Abruzzini and Abrishami discuss the limitations of the decision-making process at the end of a building's lifecycle, particularly related to the limited data available from the building's history, the difficulty in assessing the condition of a building, and the variety of stakeholders' needs to be satisfied.Authors argue that building information modelling (BIM) application can solve this problem.Kineber et al. examine the influence of value management (VM) and critical success factors (CSFs) on the implementation of VM activities in Egypt construction projects.The influence of VM CSFs on VM implementation is established, suggesting moderate effects and strong relationship between VM implementation activities and its CSFs.Prasad et al. contribute to finding some alternative cementitious material for concrete that can replace ordinary Portland cement to overcome CO 2 emissions due to the utilization of cement in the construction industry.An attempt has been made to utilize a waste material (high calcium fly ash) from thermal power plants and M-sand to produce a geo polymer concrete.This research analyses the type of binder material, molarity of activator solution and curing condition and contributes to the reduction of the largest CO 2 footprint of a single material.Onturk et al. analyse the recycling of waste
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.472 | 0.314 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".