MétaCan
Menu
Back to cohort
Record W3151370249 · doi:10.2478/euco-2021-0008

The Current Challenges and Future Alternatives of Supplying Remote Areas with Basic Goods: The Case Study of Idrijsko-Cerkljansko Region, Slovenia

2021· article· en· W3151370249 on OpenAlexfundno aff
Barbara Kostanjšek, Naja Marot

Bibliographic record

VenueEuropean Countryside · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
FundersInterregCanadian Institute for Advanced Research
KeywordsBusinessService providerService (business)Goods and servicesHuman settlementSupply and demandEnvironmental economicsEnvironmental planningEnvironmental resource managementMarketingEconomicsGeographyEconomy

Abstract

fetched live from OpenAlex

Abstract The accessibility of services of general interest (shops, post offices, banks etc.) in rural hinterlands is decreasing and villages that once supplied areas with services are losing their functions in the central settlements’ network. According to the current analytical framework the key challenges of supply are people’s dependence on car transportation, lack of village shops and other basic services, and e-services replacing location-based services. This paper examines the current dynamics of the supply of basic goods in the Idrijsko-Cerkljansko region of Slovenia. Using mixed methods approach, the paper covers a historical overview of service provision by a field survey and historical analysis, as well as an analysis of demand and supply, done via an online questionnaire. The aim of the paper is to combine the findings into a proposal for an optimized alternative supply network integrating good practices such as linking providers, promoting local products and reopening of village shops.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.326
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEuropean CountrysideSame topicCross-Border Cooperation and IntegrationFrench-language works237,207