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
In recent years, Artificial Intelligence (AI) has made significant progress in non-routine, cognitive tasks. The May 2023 CfM-CEPR survey asked the members of its European panel to predict the impact of AI on global economic growth and unemployment rates in high-income countries over the upcoming decade. Most respondents believe that AI is likely to boost global growth to 4-6% per annum. It is also stated that AI is unlikely to affect employment rates in high-income countries, with the remainder split between predicting an increase and a decrease in unemployment rates. From farming and education to healthcare and the military, AI is poised to make sweeping changes to the workplace. This paper makes an attempt to answer the question: can it have a positive impact, or are we in for a darker future? This study presents AI implementation into the labor progress model and predicts its possible influence. The suggested model is highly adaptive and will be useful both for practical and academic purposes.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it