The black and white book of knowledge: knowledge sharing in the oil and gas industry
Bibliographic record
Abstract
The oil and gas industry in western Canada has a wealth of knowledge and experience, the sharing of which is challenged by an uncertain economic environment, a naturally complex project management setting and, more recently, an aging population of baby boomers that are transitioning to retirement. There is a need to explore the oil and gas industry to understand its unique challenges and discover in what context sharing knowledge works in order to make it more effective. Experiential observations led to the postulation that there could be two versions of knowledge; a "black book" (that is least shared) and a "white book" (that is more readily shared). This research investigates this dichotomy by posing three research questions: 1.- Do industry participants perceive information and knowledge differently?; 2.- What information and knowledge are shared?; 3.- What is the context in which information and knowledge are shared?. A modified Delphi methodology was employed to survey industry participants. Results were displayed in an "information-to-knowledge spectrum", which identified a "grey area of uncertainty" regarding what information and knowledge meant to the oil and gas industry. Contradiction among the 'owner' and 'engineer' industry roles was observed and no correlation was found between the information-to-knowledge spectrum and willingness to share. However, willingness to share was found to be motivated by the perception of 'added value' or 'benefit' in the specific context of information and knowledge. This research concludes by illustrating that there is neither a black nor white book, but rather a "grey book with black and white tabs" that are movable and assigned subjectively by an individual following observation of the specific context.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".