MétaCan
Menu
Back to cohort
Record W4210335664 · doi:10.2478/auseur-2021-0011

Vision and Market Segmentation in Urban Strategy through Marketing Approach: A Case Study of Sumy City

2021· article· en· W4210335664 on OpenAlex
Anastasiia Yezhova

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

VenueActa Universitatis Sapientiae European and Regional Studies · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianMarket segmentationMarketing strategyBusinessMarketingPort (circuit theory)Competition (biology)SegmentationStrategic planningRegional scienceGeographyComputer scienceArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Abstract Competition for all kinds of resources has started among the Ukrainian cities and first of all for human capital. Therefore, a proper strategic planning is the uppermost priority for local governments. In this paper, the vision and market segmentation of the adopted strategy for the socio-economic development of the Ukrainian city of Sumy is analysed using the comparative method and content analysis. It aims to demonstrate that the city marketing approach towards strategy creation helps to make the strategy more concrete and visible. Existing city marketing strategies of Helsinki, Whitehorse, and North Port were used for comparison.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.058
GPT teacher head0.292
Teacher spread0.234 · 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