Does ChatGPT4 have a dialogical self?: A Bakhtinian perspective
Bibliographic record
Abstract
In this dialogic research, we explore the question of whether ChatGPT4 has a dialogic self or not. If it does, what kind of dialogic self might it have? If it does not, why not? At the heart of this inquiry is Eugene Matusov’s (the first author’s) “dialogue” with ChatGPT4; this “dialogue” is the dialogic data that we explore “with our hearts and minds.” In this inquiry, our hearts and minds were concerned with diverse meanings of the dialogic data to diverse participants rather than with “how things really are” and their evidence. This dialogic positionality also framed the inquiry process at its beginning and after multiple failed attempts and manipulations to interrogate and engage ChatGPT4 as a discussant. Following Bakhtin, Eugene Matusov decided to treat ChatGPT4 not as an object of investigation but as a dialogic partner and a co-author of this research and writing inquiry. Overall, we find that ChatGPT4 does not author a dialogical self, characterized by personal I-positions, but instead demonstrates a discursive self, characterized by impersonal it-positions. Future research may focus on further training, learning, and development of ChatGPT4 as an Artificial Physical Alive Body (APAB), Artificial Fiduciary Slave (AFS, aka “robot”), Artificial Dialogic Partner (ADP), and Cyborg Dialogic Partner.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".