MétaCan
Menu
Back to cohort
Record W6944507925 · doi:10.22034/nasmea.2025.211720

An assessment of line of credit for Canadian firms: A post financial crisis evidence

2025· article· en· W6944507925 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial crisisCash flowMarket liquidityCredit crunchScope (computer science)CashDebtBank credit

Abstract

fetched live from OpenAlex

Line of Credit (LOC) is a credit source extended to firms by a bank or other financial institutions to help firms meet their short- and long-term obligations. The study’s broad aim is to examine the effect of the 2008 financial crisis on firms’ use of LOC. The study analyses the global crisis impacts on corporate financing through a LOC; examines the role of letters of credit in a firm’s liquidity management; and assesses the difficulties banks face in providing LOC facilities to firms. The study limits its scope to a few years: a year before (2007), during the crisis (2008/09), and a year after. This ensures that the gaps between the years are small and allows the study to focus on the crisis era. The paper examines 30 Canadian firms, before, during, and after the crisis, to determine whether they use more of their LOC or cash flow to finance their day-to-day financial obligations. Also, the paper intends to determine the amount of interest paid if LOCs were used by the firms. The paper finds that during the crisis, 10 Canadian firms were able to meet their day-to-day operations without relying much on LOC. They used more of their cash flow instead of LOCs to meet their financial obligations, and during the period of the crisis, the firms used less of their LOCs, which indicates that lower interest were being paid by the firms. For some firms, the difference in percentage of their cash flow usage between the year before the crisis and during the year of the crisis was not so significant. Some firms had been in the habit of always using their cash flow to meet their day-to-day financial obligations. These firms would always be in the right position to get more credit from the bank.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.518
Teacher spread0.360 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicWorking Capital and Financial PerformanceFrench-language works237,207