<scp>ChatGPT</scp> Media Coverage Metrics; Initial Examination
Bibliographic record
Abstract
ABSTRACT This paper presents an overview of coverage of OpenAI's ChatGPT in media outlets from November 2022–March 2023, a comparison to previous media coverage of the chatbot Tay across the same outlets, and a count of ChatGPT media articles pertaining to government legislation and regulation. The New York Times, Wired, Gizmodo, The Globe and Mail, and The Guardian were searched for coverage. Across all five outlets there is an uptick in media coverage surrounding ChatGPT, with total numbers of included articles per month being 0 in November, 39 in December, 68 in January, 104 in February, and 143 in March. Findings exemplify the trend of increased coverage of ChatGPT in media public discourse, which contrasts with previous smaller media coverage of Tay. Examination of headlines and subheadings of included articles reveals minimal coverage (5.7%) dedicated to government legislation of ChatGPT. Future research will evaluate what is being said about ChatGPT within these media outlets.
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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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".