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Record W4409737354 · doi:10.1177/03611981251320386

Can Service Contracts Potentially Enhance the Creditworthiness of Transport Cooperatives? A Case Study from the Philippines

2025· article· en· W4409737354 on OpenAlexaff
Varsolo Sunio, Justin Reginald Nery, Sandy Mae Gaspay, Thomas Stringer

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCash flowBusinessFinanceCash managementService (business)AuditCashCash flow statementOperating cash flowFinancial systemAccountingMarketing

Abstract

fetched live from OpenAlex

We study, in an exploratory manner, the potential impact of service contracts on the cash flows and creditworthiness of two transport cooperatives in the Philippines. Annual audited financial statements for two periods (before service contracting [SC] and during SC) are examined using four financial metrics. The analysis reveals that the cash flows stabilized or improved in 2020–2022 when the two transport cooperatives participated in the SC program. The cash flow stability/improvement is a potential result of SC. We then presented the cash flow analysis to six credit officers and asked them to evaluate the creditworthiness of the transport cooperatives based on the cash flows. In general, the bank credit officers confirmed that cash flow improved after 2020 and that SC was a contributing factor to this improvement in cash flow, which has somehow enhanced the creditworthiness of the transport cooperatives as credit borrowers. The results of the study contribute to the field of financing for informal public transport reform, which is understudied in the literature. Moreover, this research is relevant for global policymakers and transport scholars, including those in regions such as North America and Europe, offering insights into how SC can enhance the financial sustainability of smaller transport operators.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0030.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.385
Teacher spread0.296 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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