Correction: AI content detection in the emerging information ecosystem: new obligations for media and tech companies
Bibliographic record
Abstract
In this article, there are several corrections as listed below,• In page 2: 'Mixtral (Jiang et al., 2024), are' must be corrected to 'Mixtral (Jiang et al., 2024) are' • In page 2:'see, e.g., [[passim]]' must be corrected to 'see, e.g.,' • In page 2: '(NYT, 2023) their capacity to produce' must be published as ' (NYT, 2023).Organisations can similarly increase their capacity to produce' • In page 2: "2024 will see democratic elections taking place" must be published as "This year, democratic elections are taking place" • The reference GPAI, 2023 was missed and must be published as
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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 | 0.006 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
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".