Towards the creation of a valid, simplified and systematic method to identify and measure the social impacts of international construction projects on the local communities in different geographical contexts
Bibliographic record
Abstract
New opportunities are arising for contractors interested in contributing not only to the economic and environmental pillars of sustainability but also to society. Integrating social sustainability practices early in the tender and execution phases of their projects denote a competitive advantage for contractors. Contractors that recognize the main social issues of the host countries early during the tendering phase, not only create economic and reputational benefits for the clients, but also are more likely to contribute to the achievement of the SDGs in each nation. Likewise, when a social impact measurement system is in place along the project execution, contractors can enhance trust with the local communities and thus maximize the possibilities to obtain and maintain a social license to operate. This research consisted of two main parts. The first part aimed to create a simplified, valid and systematic method for contractors to identify and measure the social impacts of their projects applicable in different geographical contexts; the second part was its applicability on a pilot case study. For the first part, seven recognized methods of (social) impact measurement were analyzed and synthesized to create the core of the proposed method. The method builds upon the ‘consensus’ of the main social sustainability issues acknowledged in academic literature and international agreements of organization endorsed by the United Nations. These issues refer to the business responsibility to respect human rights, ensure proper working conditions, and to engage with the local communities through different initiatives. Concerning the second part, the method was tested in a marine project located in Canada. Because the project is in its pre-execution phase, and due to different limitations encountered during the data collection process, the method was partially applied. Thus, the last two stages of the proposed method were not included in the study. As a result, the main social concerns of both, the local communities and the companies involved in the project were identified, baseline information was collected from secondary sources and indicators to monitor the impacts during the execution phase were proposed. Measuring the social impacts of construction projects remains challenging. This pilot case study evidenced that remote surveys are not an adequate method to collect empirical information in social impact studies because of risks of miscommunication. Data collection processes must be better addressed in guidance documents that aim to help companies to conduct a social impact study and in practice.
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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 | 0.158 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".