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
Newspaper sports beat reporters have experienced challenges to their workflow as social media, such as Twitter, has emerged as an essential tool in the reporting of live-game events. The purpose of this study was to assess the ways newspaper sports beat reporters meet consumers’ needs for information during these live events. Using retweets and likes as measures of engagement, this study found that newspaper sports beat reporters’ Twitter content during live-game coverage was liked and retweeted more frequently when it included analysis, opinion, entertainment, and visual content. By contrast, tweets containing only play-by-play outcomes were retweeted and liked significantly less than average. This study suggests that newspaper sports beat reporters should capitalize on their exclusivity and insider access to create Twitter content beyond mere play-by-play results that are typically available to those following the game through more traditional means such as television, radio, or in person. These strategies could distinguish newspaper sports beat reporters in an increasingly crowded sports media landscape. These findings may also be applied to the work of journalists beyond the sports realm.
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 | 0.000 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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