THE DIVERSITY GAP IN THE PUBLIC–PRIVATE PARTNERSHIP INDUSTRY: AN EXAMINATION OF WOMEN AND VISIBLE MINORITIES IN SENIOR LEADERSHIP POSITIONS
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
ABSTRACT Despite intense focus on leadership diversity in industries such as high technology, business, the media and academia, to date the infrastructure sector has not received the same level of scrutiny. This paper develops a theoretical framework to explain why leadership diversity matters in the management of complex infrastructure projects delivered through public – private partnerships, and then empirically identifies the diversity gap in senior leadership in the PPP industry worldwide. The study is based on an examination of over 2,800 public and private sector executives, board members and politicians responsible for PPPs in over 90 countries. The results show that women and racial minorities are significantly underrepresented in senior leadership roles, a pattern that is deeply entrenched and consistent globally. The paper concludes by discussing the implications of the findings for the infrastructure industry, and explores how a lack of leadership diversity can influence project management outcomes.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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