#metoo Digital Media Collection - Fourth quarter 2017
Bibliographic record
Abstract
<p>This dataset contains the tweet ids of 4,416,666 tweets, including tweets between October 17, 2017 and December 31, 2017. This collection is a subset of the Schlesinger Library #metoo Digital Media Collection.</p><p>These tweets were licensed from Twitter's Historical PowerTrack and received through <a href='https://support.gnip.com/'>GNIP</a>.</p><p>Please note that there will be no updates to this dataset</p><p>Because of the size of the files, the list of identifiers are split in 5 files containing 1,000,000 ids each.</p><p>Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Therefore, this dataset only contains tweet ids. In order to retrieve tweets still available (not deleted by users) tools like <a href='https://github.com/DocNow/hydrator'>Hydrator</a> are available</p><p>There are similar subsets related to the Schlesinger Library #metoo Digital Media Collection available in this dataverse</p>
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.295 |
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; both teacher heads agree on what is shown here.
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".