E-CARGO/RBC Research Guide: A Road Map for Researchers
Bibliographic record
Abstract
In addition to outlining the key components of the Environments – Classes, Agents, Roles, Groups, and Objects (E-CARGO) model and Role-Based Collaboration (RBC) methodology, this article aims to serve as a comprehensive research guide for scholars and researchers embarking on investigations within their research fields. By offering persuasive and illustrative arguments, the authors furnish valuable and pragmatic guidelines, equipping potential researchers with insights on selecting pertinent topics, crafting compelling scenarios, engaging in effective modeling practices, and designing rigorous experiments. The elucidation of these fundamental steps not only facilitates a clearer understanding of the intricate aspects of E-CARGO and RBC but also provides a road map for researchers to navigate the intricacies of conducting solid and insightful research within these frameworks.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.049 | 0.053 |
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".