Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
362,464,578 tweet ids for tweets directed at Donald Trump (@realDonaldTrump), collected with Documenting the Now's twarc. Tweets can be “rehydrated” with Documenting the Now’s twarc, or Hydrator. twarc hydrate to_realdonaldtrump_20210120_ids.txt > to_realdonaldtrump_20210120.jsonl. Collection notes: Tweets from May 7, 2017 - October 16, 2018 of the dataset used a combination of the Filter (Streaming) API and Search API. The Filter API failed on June 21, 2017. From June 23, 2017 forward only the Search API was used to collect. Collection was done every 5 days on a cron job, and periodically deduplicated. There is a data gap from Tue Jul 28 13:53:50 +0000 2020 through Thu Aug 06 09:36:23 +0000 2020 due to a collection error. This dataset also includes a number of derivative csv files from the original jsonl collected. This includes: A user csv file created with jq (see below). twut userInfo twut language twut times twut sources twut hashtags twut urls twut animatedGifUrls twut imageUrls twut mediaUrls twut videoUrls User csv: jq -r '[.id_str, .created_at, .user.screen_name, .retweeted_status != null] | @csv' to_realdonaldtrump_20190130.jsonl > to_realdonaldtrump_20190130_users.jsonl
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it