Bibliographic record
Abstract
In looking for best practices in road construction, firms will need to investigate methods of knowledge building and learning as a way to achieve best compliance with corporate governance directives. This paper presented an analysis of an oil company as a learning organization through observations in order to make recommendation to build knowledge capacity. Observation and analysis of oil and gas construction workers was conducted over many years of working alongside them. A review of literature that pertained to road ecology, best practices, learning, knowledge transfer and corporate governance was conducted and gaps were identified. These gaps are between how companies utilized the knowledge of workers to improve their practices, stir innovation and increase knowledge of governance mandate when compared to the literature. This paper makes several suggestions on how to close the gaps. First, it is important to define what best practice is for your organization. What is best is contextual. Secondly, it is critical to review the structure of the organization to align it for knowledge transfer. Finally, it is necessary to have support and alignment of the top management to be a learning organization as the prize for success is huge.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".