Bibliographic record
Abstract
Information Technology (IT) has been identified as a driver of productivity. Despite tremendous advances in IT and its extensive adoption, productivity gains in developed economies have fluctuated. One area of IT that has received much attention recently is Artificial Intelligence (AI). Artificial intelligence as a recognized discipline is almost seventy years old and we are now at the point where forty per cent of the global workforce is exposed to artificial intelligence. Much of this artificial intelligence is not meant to perform cognitive tasks, rather it is meant to augment the task of the user. We now stand on the edge of possibly huge increases in productivity due to the impact of Generative AI. Generative AI’s capabilities are engineered to perform cognitive tasks. As such, Generative AI is meant to complement the user. While much has been written about the anticipated growth in productivity due to Generative AI, not as much has been written about the potential impact on global employment. This paper reviews the relationship between IT and productivity and the potential impact of Generative AI on employment. While Generative AI has the potential to complement knowledge workers with higher education and skills, there is a danger of displacing some workers without such education and skills.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".