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Record W6948873623 · doi:10.5255/ukda-sn-6888-20

Small- and Medium-Sized Enterprise Finance Monitor, 2011-2017

2018· dataset· en· W6948873623 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUK Data Archive · 2018
Typedataset
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsQuarter (Canadian coin)Sample (material)Survey data collectionTelephone surveyBusiness risksSmall business

Abstract

fetched live from OpenAlex

The <I>Small- and Medium-Sized Enterprise Finance Monitor</I> survey is commissioned by the Business Finance Taskforce to provide an independent and authoritative report into the key issues of SME Finance. 4,500 telephone interviews are conducted per quarter (5,000 interviews prior to 2016), across the UK, to a carefully structured sample of SMEs by size, sector and region. Data for Q4 2017 was collected between October and December 2017. The data set includes all data collected for the last 10 waves, i.e. from Q3 2015 until Q4 2017, whilst the report focuses on data gathered from the last 4 quarters. The report is now released biannually; the latest report is submitted now for Q4 2017, and the next one will follow after Q2 2018.<br> <br> The survey explores demand for external funding amongst SMEs and the response to requests for funding made to banks in the last 12 months. It also asks for future finance needs and assesses business confidence, growth, and barriers to growth for the future, as well as the impact of a lending experience on the overall banking relationship. As well as identifying the proportion of SMEs that have approached a lender for external finance, the survey identified those who would have liked to apply, but haven't, the barriers to such an application, and the impact of the decision not to seek funding on business performance.<br> <br> A wide range of business demographics are collected to allow for sub-group analysis by criertia such as age of business, external risk rating, type of facility requested, and the “formality” of the business (planning, HR policies, importing, exporting, etc.).<br> <br> The intention is for this to become the definitive data set on this topic for banks, government, business organisations and other interested parties, including academics. It is hoped it will be used to provide answers, to obviate the need for similar quantitative research, and to provide the starting point for spin off projects into specific aspects of SME Finance.<br> <br> Further information is available on the <a href="http://www.sme-finance-monitor.co.uk/" title="SME Finance" target="_blank">SME Finance Monitor</a> web page.<br> <br> <b>Latest Edition Information</b><br> For the 19th edition (May 2018) additional data and documentation were deposited to extend the coverage to Quarter 4, 2017.<br> <br>

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0040.007
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.043
GPT teacher head0.302
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it