Bibliographic record
Abstract
Roles remain nebulous entities, notwithstanding their extensive interdisciplinary research. This paper argues through a meta-ontological conceptual tool of grounding that there are three key facets of roles: a role position, a role specification, and a role potential. A foundational perspective on roles can be specified by “role choices” as to which facet of roles is primary. Role choices are illustrated with theories of roles that are built in compliance with four well-known upper ontologies: GFO, DOLCE, BFO, and UFO. The relationship between such three facets of roles and the GFO-based three kinds of roles (relational, processual, and social) is closely examined. These three facets are also comparatively studied from linguistic (e.g. ‘have a role’ versus ‘play a role’) and methodological (realism versus conceptualism regarding ontology design) perspectives. Furthermore, the family resemblance view of roles as “epistemic trackers” is proposed: the general notion of role is merely (partially) unified by its three facets and helps to keep track of some entity with respect to its role-related aspects. Finally, defining characteristics of roles in conceptual modeling are considered in terms of the three-facet theory. This work provides the grist for future practical development of an ontological module for generic role representation.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".