Employee and Workforce Adaptation as a Result of Artificial Intelligence Deployment
Bibliographic record
Abstract
Over the last decade (2010-2020), society saw many technological advancements that significantly impacted our lives and forever changed the world and its perception. Various algorithms are now embedded in our lives and have the ability to drive consumer behaviour. Artificial Intelligence has the advantages of rapidly digesting large volumes of data, exposing trends and patterns utilized for personalization and customization, and achieving the best possible outcome. However, society lacks the understanding of the price that will be paid for the adaptation to new business models. This research investigates the current trends and predictions for labour market changes and their societal impact and provides recommendations aimed at managing and adapting the workforce to the societal changes that resulted from the wide adoption of AI-powered tools. Additionally, this study describes the consequences of AI implementation and its effects on the labour market and outlines the threats of the significant increase in the unemployment rate using qualitative analysis of the secondary data. Recent publications from industry experts are reviewed, and predictions for the labour market are synthesized.
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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 | 0.019 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".