Closing the Diversity Gap in the Infrastructure Industry
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.
Bibliographic record
Abstract
This paper examines the diversity gap in the infrastructure industry, outlining a variety of statistics and indicators that show how women and racial minorities are underrepresented in leadership positions. The infrastructure industry consists of the government departments and private firms that together build large highways, transit lines, hospitals, water treatment plants, schools, recreation centres, courthouses, and prisons. It also includes the companies that are engaged in the rapid development of the next wave of smart-city technologies. This industry has not received the same level of scrutiny for its lack of diversity as high-profile sectors such as high tech, entertainment, business, and academia. Yet the infrastructure industry is a major source of employment, and the projects have a profound impact on the economic prosperity, equity, and environmental sustainability of the places in which they are built. The current burst of urban infrastructure development around the world, as well as innovations in mobility, security, waste disposal, information technology, and smart-city infrastructure, will set cities on a path towards either inclusive prosperity or further social inequality. It is imperative that the leading decision makers in the industry are representative of the wider communities in which major infrastructure projects are planned, built, and operated. To this end, the paper identifies strategies to increase diversity in the administration of infrastructure, including increasing the pipeline of diverse talent, encouraging diverse hiring, changing workplace and industry culture to support diversity, and creating policies that support retention and promotion of a diverse workforce.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it