Bibliographic record
Abstract
The purpose of the investigation conducted was to discover trends in twitter popularity regarding different areas of science. This investigation can benefit areas of marketing such as targeted advertising, as well as demographic research in order to correctly test certain demographics and obtain research grants. Results included possible confirmation of our motive through principal component analysis, The data was compiled using RStudio and was narrowed down by subjects, Altmetric scores, and countries. The data was parsed through to find Key words in the abstracts of articles. Principal Component Analysis was applied to a matrix of padded tweet dates, arranged by subject. These arranged dates were also plotted to visualize trends over time. From the data collected, the articles that were most tweeted about, between January 1st, 2016 to July 1st, 2016, worldwide were articles concerning physics. Out of all the articles, ”death” was the keyword most popular in articles’ abstracts. Disease-related words appeared far more often than the word ”cure”. The United States of America, Canada and Great Britain had the highest number of tweeters. Great Britain’s population was mainly interested in articles regarding dentistry, while Canada and the United States of America had a higher tweet count in articles related to health science.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".