Better understanding financial resilience through an innovative toolkit
Bibliographic record
Abstract
This document summarises the main points discussed at the workshop “Better understanding financial resilience through an innovative toolkit” organised by the University of Essex, in collaboration with CIPFA South East (SE), where participants had a hands-on opportunity to explore the toolkit and engage in open discussions and breakout groups to exchange knowledge about financial resilience. Our evidence-based toolkit is being developed by Ileana Steccolini, André Lino, and Bernard Dom. The workshop was facilitated by Jeffrey Matsu (CIPFA), André Lino (University of Essex) and Bernard Dom (Nottingham Trent University), and funded by the University of Essex internal Policy Support Fund programme. The programme is a University’s strategic initiative to increase and speed up the beneficial social, economic, policy, and environmental impacts of our research at home and abroad.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".