Commentary on: Suzanne McMurphy's "Trust, distrust, and trustworthiness in argumentation: Virtues and fallacies
Bibliographic record
Abstract
This paper is part of an increasingly rich contribution to argument studies from disciplines studying human interaction in general. The paper is an invitation, rather than an argument, and my response is to accept the invitation. The paper offers current empirical data and theoretical considerations to ground our discussion of trust. It also invites us to consider some specific questions about how argumentation theory might incorporate this new information. I shall offer a preliminary exploration of where this might take us. Some interesting consequences emerge in the presentation of the empirical data. The historical emergence in the 1960s of trust as a focus of interest coincides surprisingly well with the beginning of a slide away from a belief in rationality as a universal and teachable human capacity (e.g. Scribner, 1977; Hamill, 1990). I’m curious what triggered this renewed interest in trust, and what it might say about our construction of relationships. It is possible that comparing the history of trust studies to the history of argument studies could illuminate shifting ideals from impartial objectivity to interpersonal connections. The framework of trust is one in which relationships take centre stage. We cannot look at an argument without looking at who has given it, when, and in the context of what relationship. This is not new to argument theory (e.g. Warrenburg, 2009) but it is still a step away from where argument theory likes to focus. It raises the question of how to re-integrate trust and trustworthiness. McMurphy’s example is that professionals or politicians may not ask themselves whether they are trustworthy, but only how they can gain the trust of a client or constituent. Is the problem that they are building a one-directional relationship, trying too hard for affective trust, when they should be creating a bi-directional relationship that leaves the client free to develop cognitive trust if appropriate? To complicate matters, trust’s neurobiological component might be created just by the appropriate administration of an oxytocin spray. That prospect has to trouble us as argumentation theorists: if argument cannot prevent misplaced trust, and if trust can be created without the effort of argument, then how can argument influence people’s actions? The cited studies of trust in negotiation and in cooperative games reinforce this worry, since they suggest that some goals of negotiation, co-operation, or argument may be unconscious. Argumentation assumes that the goals and structure of an argument are subject to conscious control. If we do engage in behavior which surprises our conscious selves when we
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.043 | 0.055 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".