Urban Planning Academics: Tweets and Citations
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
This article discusses the relationship between Twitter usage and scholarly citations by urban planning academics in the U.S. and Canada. Social media and academic publications may be considered separate activities by some, but over the past decade there has been a convergence of the two. Social media and scholarship can be complementary not only when social media is used to communicate about new publications, but also to gather research ideas and build research networks. The analysis presented here explores this relationship for urban planning faculty using data for faculty who had active Twitter accounts between March 2007 and April 2019. Measures of Twitter activity were combined with Google Scholar citation data for 322 faculty with Twitter accounts. As expected, the results highlight that there are different patterns of Twitter activity between junior faculty and senior faculty both in terms of proportions of each rank using Twitter as well as activity levels on the social media platform. The results also suggest that Twitter activity does not have a statistically significant relationship with overall scholarly productivity as measured by citation levels.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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