Bibliographic record
Abstract
President's column In the past decade, an increasing number of organizations have been creating networks, mentoring programs, and coaching and leadership training for women in the workplace. Today, women represent a significant portion of the educated talent pool in most of the developed and emerging world in a broad range of industries, lead countries and companies, and hold an unprecedented amount of power in their hands and minds. The oil industry is not an exception. From field technicians to engineers and geologists, and to vice presidents and presidents, women are making huge contributions to this historically male-dominated industry. According to the US Labor Department, the women in the US workforce, for the first time surpassed the men by 800,000 in January 2010. Canada achieved this milestone in 2007. And as I am writing this column, four female astronauts are orbiting the earth on the space station, another historical milestone, and a giant step for womankind! While there have been remarkable gains, the number of women in the science and technology industries is less impressive. For example, the share of women employed in the US oil and gas industry stands at a mere 17.5% according to a recent report by Catalyst. Another recent study conducted in the US, sponsored by the National Science Foundation, the Letitia Corum Memorial Fund, the Mooneen Lecce Giving Circle, and the Eleanor Roosevelt Fund, shows that while the number of women in science and engineering is growing, men continue to outnumber women, especially in the leadership roles. The study also reveals that fewer women than men major in science and technology fields. By graduation, men outnumber women in nearly every science and engineering field, earning only 20% of bachelor’s degrees awarded. The number of women declines further at the graduate level and even more in the transition to the workplace. The same study points at evidence that social and environmental factors contribute to the underrepresentation of women in science and engineering. Many associate science and technology fields with “male” and humanities and arts fields with “female.” The striking disparity between the numbers of men and women in science, technology, engineering, and mathematics has often been considered as evidence of biologically driven gender differences in abilities and interests! Such perceptions adversely affect women’s decisions to pursue interest in science and technology areas. As I travel the world and speak to SPE members, I have been asked whether our industry provides opportunities for a woman to develop a meaningful professional career. The question is even more alarming when it is posed by female students who are already enrolled in petroleum engineering departments or female young professionals who have been employed and engaged in the industry for some time. We have a challenge to address, one that can seriously limit the industry’s access to a significant talent resource, adversely affect its image, and consequently threaten its ability to grow. I suggest that both women in the industry and the industry itself have important roles to play in resolving these issues.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".