Bibliographic record
Abstract
In this paper, we introduce a new concept to the field of scientonomy, that of authority delegation. Authority delegation is, in essence, a type of relation between distinct scientific communities whereby one community both recognizes another as an expert on a particular topic and will accept the theories it is told by the expert community over the same topic. Importantly, authority delegation is not a new fundamental ontological category along with theory and method. We show that authority delegation is reducible to the more basic concepts of theory and method. Furthermore, we suggest that authority delegation comes in two forms: one-sided authority delegation and mutual authority delegation.Suggested Modifications[Sciento-2016-0003]: Accept the notion of authority delegation:Authority Delegation ≡ community A is said to be delegating authority over topic x to community B iff (1) community A accepts that community B is an expert on topic x and (2) community A will accept a theory on topic x if community B says so.[Sciento-2016-0004]: Provided that the preceding modification [Sciento-2016-0003] is accepted, accept the following notions of mutual and one-sided authority delegation, as subtypes of authority delegation: Mutual authority delegation ≡ communities A and B are said to be in a relationship of mutual authority delegation iff community A delegates authority over topic x to community B, and community B delegates authority over topic y to community A.One-Sided authority delegation ≡ communities A and B are said to be in a relationship of one-sided authority delegation iff community A delegates authority over topic x to community B, but community B doesn’t delegate any authority to community A.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".