Bibliographic record
Abstract
If you want ECAs on your team, you'll want them to understand how you do things. You'll also want to understand how they do things. Ideally your team's actions should be synchronized, yet complementary to some degree. That is, you seek an efficient and feasible division of labour. The ``you-things'' and the ``they-things'' have to be complete (they have to cover all the sub-tasks leading to the goal), and as sound as possible (any overlap decreases efficiency). This paper argues that, due to the impenetrability of beliefs, an artificial agent will be unable to join a group with such synchronized diversity by attempting to find a balance between its own beliefs and preferences and others' beliefs and preferences (i.e., a theory of mind). Instead, successful group membership requires ignoring individual utility, and taking actions to make the world (an everyone in it) as predictable as possible. Agents will be more predictable if they not only do the ``they-things,'' but also make it clear to others what those things are. However, the group's goals are shaped by the actions of its members, and so a boundary that identifies group membership is necessary. In essence, all agents must be able to identify to which group they belong. After filling in the argument, I give a short introduction to an emotional identity theory that may provide a way forward, and attempt to convince you that you will want ECAs on your team, but only after solving this division of labour.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".