Civil Society Chatbots: A Plurality of Conceptual Approaches
Bibliographic record
Abstract
This article examines chatbots that have been developed by civil society for social, political, and/or cultural purposes. I ask what conceptual approaches have influenced the design of civil society chatbots and how these frameworks are actualized in practice. To do this, I first propose the concept of “civil society chatbots” to understand how and why civil society has chosen to use chatbots to support their campaigning and advocacy work. I then examine four conceptual approaches that have influenced them in the design of their chatbots and illuminate each with short case studies. The conceptual approaches under study are entertainment education, community media and technology, feminist design, and human and digital rights. Using these approaches helps situate a practice that is increasing and plural within civil society but relatively eclipsed by the hype around generative artificial intelligence chatbots.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.014 | 0.067 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".