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Record W4386884422 · doi:10.32920/24171432

Theoretical Foundations in Support of Small and Medium Towns

2023· preprint· en· W4386884422 on OpenAlexaff
Eric Vaz

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFraming (construction)Knowledge managementSustainabilityMultidisciplinary approachBusinessOpen innovationPromotion (chess)Context (archaeology)Management scienceSociologyPolitical scienceEngineeringComputer scienceGeographySocial science

Abstract

fetched live from OpenAlex

<p>This theoretical review aims to create a comprehensive and systematic analysis based on previously published literature explaining how contemporary technological developments may promote new paths for small and medium-sized towns (SMTs) and their networking systems. Much has been said concerning the capacity of towns to absorb strategic knowledge, which is highly dependent on local governance systems. In this paper, five levels of multidisciplinary approaches will be addressed so as to pinpoint the theoretical grounds for the promotion and advocacy of small and medium-sized towns (SMTs) as major drivers of regional sustainability: agglomeration advantages and networking efficiencies—representing strict economic accounting of cost and benefits; clustering in a context of online environments, and its extension to open networking systems; sustainable innovation processes for SMTs, technology, and knowledge transfer in open innovation systems—both settings for discussions within the framing of new technological developments and artificial intelligence; knowledge and new technological developments with local spillovers—to be enhanced employing new educational programs and learning diffusion at advanced levels; the social functions of small and medium-sized towns—to be addressed in the areas of sociology, architecture, and planning.</p>

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
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 head0.086
GPT teacher head0.353
Teacher spread0.267 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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