MétaCan
Menu
Back to cohort
Record W7087741969 · doi:10.7202/1118938ar

Legal Tools for Urban Regeneration

2024· article· en· W7087741969 on OpenAlexvenueno aff

Bibliographic record

VenueSens public · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicKruppel-like factors research
Canadian institutionsnot available
Fundersnot available
KeywordsUrban regenerationIntervention (counseling)Public goodPower (physics)Process (computing)ShareholderDemocracyControl (management)

Abstract

fetched live from OpenAlex

The contemporary debate, in terms of urban redevelopment, has identified common goods as its reference model, outlining a way of intervention that provides the direct participation of citizens in forms of management based on self-government, direct democracy and the absence of imposed hierarchies. In these terms, private law, overcoming urban planning and public law, can help build a civic community in which innovation processes that are not directly attributable to the power of the administrator arise. In this sense, the paper intends to investigate three legal instruments that could facilitate the implementation of these processes: The Common Good Foundation, which basically consists in deducing in the private legal form of the open foundation not only a complex of passive assets, but also a collective activity and subjectivity. The trust, which can be useful for accelerating processes, making them more competitive in terms of efficiency, transparency, and targeted use of resources from where the public cedes powers to the private sector. The SPAB (Società per Azioni Buone), a newly born instrument in Favara (Agrigento), which aims to create a company open to everyone: every citizen can be a shareholder and therefore owner of a small piece of town. Bottom-up practices that aim to give voice to citizens’ needs are the lifeblood to support and implement the various urban projects. The tools described in this paper want to stimulate this scenario of regeneration and therefore to implement a practice of redemption of public spaces by citizens.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.010
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.039
Scholarly communication0.0070.008
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSens publicSame topicKruppel-like factors researchFrench-language works237,207