Post-Public AI: Research in Groups and Teams
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
The 2022 public release of ChatGPT marked a key moment in human interaction with artificial intelligence (AI), driving the widespread adoption of generative AI tools. This special issue examines how AI diffusion demands a reconceptualization of group and team research. We propose a distinction between pre- and post-public AI eras, noting that today’s teams increasingly include members with everyday AI experience. Building on Katzenbach and Smith’s classic definition of teams, we stress the need to integrate human-AI dynamics, which introduce new complexities in team composition, complementary skills, shared purpose, and accountability. We advocate for openness to diverse approaches, greater methodological innovation, and interdisciplinary partnerships to keep pace with AI-driven change. The issue features two contributions: one examining how AI types shape trust and attention, another assessing AI-driven methods for analyzing team communication. We hope this editorial sparks a broader conversation to advance group scholarship in the post-public AI era.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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