Negotiation, Compromise, and Collaboration in Interpersonal and Human-Computer Conversations
Bibliographic record
Abstract
The meaning of any message from Clyte had to be ne-gotiated the way a company of soldiers negotiates a minefield. —Danni Hubson, Post-modernism: A novel Thus a ‘hockey game ’ is nothing more than a discourse between two ‘teams ’ in order to negotiate the desig-nation of one team as ‘winner ’ and one as ‘loser ’ by means of the construction of a ‘final score’. —Fraser Stegg, Canada as a Social Construction 1 The repair of failure to understand People are very adept at recognizing when something they said has been misunderstood by a conversational partner and at recognizing when they themselves have misunderstood something that was said earlier in the con-versation. In either case, they will usually say something to repair the situation and regain mutual understanding. The same is true of non-understanding. If computers are ever to converse with humans in natural language, they must be as adept as people are in their ability to detect and repair both their own occasional misunderstandings and also those of their conversational partner—perhaps even more so, as this skill will be needed to compensate for the likely deficiencies of computers in other aspects of under-standing, which will lead to frequent misunderstandings and non-understandings on each side. The processes through which conversational repairs take place include negotiation, collaboration, and con-struction of meaning. They can be seen in examples such as the following fragment from the London–Lund
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".