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Record W4248204414 · doi:10.32920/ryerson.14656590.v1

Manifesting Civic-ness

2021· preprint· en· W4248204414 on OpenAlexaff
Heather Breeze

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
FundersAustralian Government
KeywordsCivic engagementCitizenshipRealmIndividualismPublic spaceDilemmaSpace (punctuation)Perspective (graphical)SociologyBridge (graph theory)Political scienceGood citizenshipPublic relationsPublic administrationEpistemologyPoliticsLawComputer scienceEngineering

Abstract

fetched live from OpenAlex

Urban centres have seen decreasing public notions of civic-ness, as citizens’ understanding and implementation of civic engagement have shifted into the individualistic and private physical realm. The characteristics of a citizen in the contemporary age are scattered and ill-defined, leading to a dilemma of citizenship, and where and how civic engagement takes place. Analyzing this quandary from an architectural perspective begins to question how a space can become civic, and addresses the necessity of physical space for increased civic engagement. This thesis aims to define and suggest a bridge for the current gap in civic architecture that is citizen-oriented, combining programmatic and spatial functions as an architectural alternative to highly institutional governmental spaces. The alternative provides a platform of tangible, non-privatized spaces that have the potential to make room for a more balanced approach to participation that encourages the engagement of a substantive citizenry.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.016
Scholarly communication0.0130.009
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.012
GPT teacher head0.199
Teacher spread0.188 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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