Bibliographic record
Abstract
In research conducted using Twitter data, informed consent has taken the back seat. This literature review examines the perspectives of users, researchers and research ethics boards to provide nuance and context to the issue. Users are generally unaware that their data can be taken for research purposes and that they have agreed to be studied within the platform’s terms of service. This is concerning for both researchers and users alike, as it continues to blur the line of public and private information. Users want to be informed when they are being studied. When informed consent is not obtained, researchers are not respecting the data and the humans who created it. If researchers were required to obtain informed consent when engaging with Twitter data, the resulting research would be more ethical and protect everyone involved: the researcher, the user, and the university.
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.
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 | 0.277 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".