Introduction to the Special Issue on ChatGPT and other Generative AI Commentaries: AI Augmentation in Government 4.0
Bibliographic record
Abstract
This special issue is for providing a forum for rapid sharing of current and potential transformations and disruptions that may be enabled by GenAI in the government domain. The main objectives of the Special Issue are: to gauge the potential impact of powerful GenAI tools, such as ChatGPT, Gemini, Co-Pilot, and MetaAI, for the future government and democracy, including negative and positive effects; to assess the role of the Government in the face of proliferation of GenAI tools as a potential facilitator of innovations towards a better society or as regulator to harness the new generation of AI and to mitigate risks; and lastly, to envision how the public sector workplace may need to adapt to the changes, identifying top priority requirements and strategies for the new governing models.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.125 | 0.043 |
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".