Digitalization and the Future of Employment: A Case Study on the Canadian Offshore Oil and Gas Drilling Occupations
Bibliographic record
Abstract
This paper presents a novel approach to identifying reskilling requirements, job merging pathways, and a tentative timeline for transforming offshore oil and gas drilling occupations amid the fourth industrial revolution (industry 4.0). The proposed algorithm focuses on potential job merging due to technological adoption. It introduces a scaling factor named digital readiness level to incorporate modulation factors (e.g., cost of development and deployment of new technologies, labour market dynamics, economic benefits, regulatory readiness, and social acceptance) that act as catalysts or hindrances for technology adoption. A feature-based approach is developed to assess the similarities between occupations, while a mathematical model is developed to project automation trajectories for each job under investigation. These facilitate the consideration of potential job merging scenarios and the associated timeline. Since technology adoption depends on the industry, region, occupation, and stakeholder’s ability to manage the transformation, the proposed algorithm is presented as a case study on Canadian offshore oil and gas drilling occupations. However, this algorithm and approach can be applied to other industries or occupation structures. The proposed algorithm projects that the total number of personnel on board (POB) in a typical offshore drilling platform will be reduced to six by 2058. A sensitivity analysis was conducted to assess the robustness of the proposed algorithm against variations in the feature values and weighting factors. It was found that when changing feature values and weighting factors up to$\pm 20\%$of their original values, only one job that remains after 2058 follows three different job merging pathways, while others remain unchanged. Even the job that followed three different pathways was composed of the same source jobs compared to the corresponding job in the baseline results.Note to Practitioners—This research is inspired by the ongoing digital transformation initiatives and their socioeconomic impact. The adoption of digital technologies, such as automation, robotization, digital twins, data-driven decision-making systems, smart devices, and cloud computing technologies, gradually transform existing workplaces into digitally-enabled smart workplaces. Therefore, stakeholders must invest in training programs to reskill existing workforces and to orient prospective employees to work at these smart workplaces. If technology adoption occurs at a rapid or slower pace than workforce reformation, industries cannot gain the optimum benefit from their digital transformation initiatives. Also, human capital investments may not generate much benefit if technology adoption and workforce reformation occur at different rates. Therefore, this work presents a novel framework to predict future employment scenarios, particularly for the workers in offshore oil and gas drilling activities, along with a tentative timeline. Stakeholders can utilize the proposed framework to effectively plan the pace of technological adoption, future workforce transformation and human capital investments.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".