3rd quarter report of 2018 to the Minister for Business, Enterprise and Innovation detailing the analysis and performance of the SME Credit Guarantee Scheme 2017 at 30th September 2018
Bibliographic record
Abstract
The Credit Guarantee Scheme (CGS) provides a State guarantee through the Department of \nBusiness, Enterprise & Innovation (the ?Department?) to accredited Lenders (Allied Irish \nBanks, Bank of Ireland and Ulster Bank Ireland) of 80 per cent on eligible loans or Performance \nBonds to viable Micro, Small and Medium-sized Enterprises (SMEs). The Guarantee is paid by \nthe State (the ?Guarantor?) to the Lender on the unrecovered outstanding principal balance \non a Scheme Facility in the event of a Borrower defaulting on the Scheme Facility repayments. \nThe purpose of the Scheme is to encourage additional lending to SMEs, not to substitute for \nconventional lending. SMEs are thus enabled to develop a positive track record with the \nLender with the objective of returning to standard commercial credit facilities in time. It will \nalso place Irish SMEs on a competitive level-footing relative to other trading competitors who \nare able to avail of a guarantee in their own countries. \nIt is important to note that funds provided under the Scheme are neither a grant nor a \nsupport for ailing businesses or customers in difficulty. All decision-making at the level of the \nindividual Scheme Facility is fully devolved to the participating Lenders. \nThe Credit Guarantee Scheme 2017 became operational in July 2018. The Department has \nappointed the Strategic Banking Corporation of Ireland (SBCI) as Operator.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.195 | 0.164 |
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".