Consequences of AI in the workforce: How AI is taking our Jobs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although artificial intelligence is helping solve many problems in today's world, it is also the reason many people around the world are losing their jobs. AI has the ability to replace or reduce many jobs now and in the future due to the rapidity of its growth. Types of jobs that could be harmed include white collar professions, unlike the blue collar careers that were reduced due to previous technological advancements. The advances in AI have mostly targeted jobs relating to digital art, content creation, and programming, meaning that these careers are most likely to see a reduction due to the growing use of AI. This article aims to review how AI has evolved throughout the years and the consequences it has on the economy as a whole. By taking a look at how AI replaces jobs, we can provide insight on how we can prepare for an increase in the use of AI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it