Bibliographic record
Abstract
Q&A What is the mission of Getenergy? Getenergy was originally created on the fringes of the Offshore Europe exhibition in Aberdeen in 2003. I was asked by the British government to run a meeting on education and training for 20 staff from national and international oil companies who were visiting the show. The meeting clearly demonstrated that the upstream industry desired a dedicated event entirely focused on education and training. Getenergy (Global Education and Training for Energy) was the company that emerged in 2004 following this discussion. Getenergy was founded by accident, not design, based on a niche, yet clear, industry need. Getenergy does not provide or sell training; rather it is a platform for those who do, to meet the needs of those who require often complex training and education solutions, particularly in developing hydrocarbon provinces, where training is both part of the contractual obligation for IOCs and at the heart of the national economic development agenda. Are most of your events designed to bring together NOCs, IOCs, academia, and the service sector? Rather like preparing drilling muds, our meetings require all the right ingredients in the correct quantity. We seek, in every event, to have an equal number of universities, local colleges, training providers, and national and international oil and gas company representatives. We have a Learning Arena exhibition, which allocates space to exhibitors representing the world’s largest oil companies or the smallest university departments. In many of our meetings, universities from brand-new hydrocarbon provinces participate in what often represents the first international meeting they have attended. You have an upcoming training event in London. What will the focus be? How is it unique? How many countries will be represented? In addition to regional and national meetings Getenergy has, since 2005, organized an annual global meeting. This is really our flagship event, which has taken place in Houston (2005), Dubai (2006–07), London (2008–09), Kuala Lumpur (2010), Calgary (2011), and London (2012–13). The next global meeting is in London during 3–5 June and will see participation from those concerned with education, training, and technical learning delivery from 45 countries for 3 days of workshops, our Learning Arena Exhibition, and our “Power Point-free” Exchange sessions. We will have delegations from universities, institutes, and the oil and gas industry in Brazil, Ghana, Kazakhstan, Kurdistan, Malaysia, China, Mozambique, Nigeria, and Tanzania, among others. Our annual global event is unique not only in terms of audience, but also the detailed discussions among national and international oil and gas companies and the global education and training community that take place.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.400 | 0.186 |
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".