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Learning Participatory Citizenship

2013· book-chapter· en· W25086857 on OpenAlexaffabout
Behrang Foroughi, Erica Thekla McCollum

Bibliographic record

VenueSensePublishers eBooks · 2013
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of British ColumbiaSt. Francis Xavier University
Fundersnot available
KeywordsCitizenshipCitizen journalismCorporationPublic relationsCorporate governanceState (computer science)Work (physics)Public housingPublic administrationPolitical scienceSocial engagementSociologyBusinessEngineeringSocial science

Abstract

fetched live from OpenAlex

This chapter explores the informal learning of tenant volunteers who represent their communities in the Tenant Participation System (TPS) of Toronto Community Housing Corporation (TCHC), the largest public housing provider in Canada. The Tenant Participation System is a pioneering state-sponsored practice of communitybased social housing governance in a major urban setting. The Tenant Participation System represents a model for more direct citizen engagement as it provides opportunities for tenants to work with each other and with management to address issues affecting the community. As tenants take on new responsibilities and tasks, this participation provides an opportunity for the development of new skills, attitudes and knowledge. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.013
Scholarly communication0.0070.008
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.003

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 head0.106
GPT teacher head0.236
Teacher spread0.130 · 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 designQualitative
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

Citations2
Published2013
Admission routes2
Has abstractyes

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