Bibliographic record
Abstract
Context for this document: #AllAtlantic CLUSTER of Project Managers run between 2021-2024 by AtlantECO & Mission Atlantic and attended by 15+ concurrent projects, identifies a series of barriers to meaningful clustering of fully funded parallel projects, all explicitly supporting the #AAORIA Priorities on research, as well as cooperation with Atlantic states. Follow-up conversation(s) between EC Project Officer for Mission Atlantic, Isabel Pinto, CIIMAR, & Ivo Grigorov (26 Sept 2024) and multiple HorizonEU Consortia submitting to the 18th Sept 2024 Deadline (to be disclosed 20 Dec 2024) identified a series of existing and tested mechanisms for structural clustering between concurrent projects. This document aims at highlighting lessons learned since 2013 with actionable advice for both EC REA colleagues implementing multi-project portfolio, and future applicants to FP9 and FP10.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.049 | 0.027 |
| Insufficient payload (model declined to judge) | 0.088 | 0.016 |
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".