A systematic review of scholarship in AI and communication research (1990-2022)
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
Communication scholarship in the area of artificial intelligence was sporadic in the early 1990s but is witnessing a significant increase today. A systematic review of 197 articles collated from a search of communication journals in the Web of Science (WoS) database from 1990-2022 reveals a spike in published articles with topics and keywords “artificial intelligence and communication” from four articles in 2017 to 65 articles in 2021. Thirty articles were already published by the time data collection ended in the first quarter of the year (April 2022). Findings reveal a majority of these studies utilize qualitative methods (118), quantitative methods, (42) and then multi- or mixed-methods (34). Frequency analysis reveals a significant amount of international scholarship with 70 published articles. Key topics of research are grouped into ten areas of focus with important conceptual contributions by key scholars during this period. The researchers conclude with limitations and future directions.
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 | 0.056 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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