MétaCan
Menu
Back to cohort
Record W2809679051 · doi:10.1080/00396265.2018.1482674

Hybrid governance and land purchase strategies in a state-subsidised housing project in a rural South African town

2018· article· en· W2809679051 on OpenAlexafffund
Michael Barry, Lani Roux

Bibliographic record

VenueSurvey Review · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsState (computer science)Land registrationCorporate governanceRegister (sociolinguistics)Unit (ring theory)BusinessLand administrationLand tenureEconomic growthPublic administrationAdministration (probate law)Political scienceGeographyFinanceEnvironmental planningEconomicsLaw

Abstract

fetched live from OpenAlex

Many of the estimated three million state-subsidised houses that South Africa has delivered to the poor in registered ownership since the end of apartheid in 1994 have changed hands off-register, creating cloudy titles. This is indicative of effective tenure administration being ranked below other policy goals without suitable strategies to address the shortcomings of granting ownership as the tenure form upfront. Interviews with residents, community leaders, officials, and land professionals indicate that buyers register transactions if the registration system is accessible, the family unit believes registration is useful, and that they are aware of the risks of off-register transactions. The South African land governance system is somewhat incoherent, with various hybrid official and unofficial arrangements. In this environment, community-based organisations (CBOs) may unwittingly encourage off-register transactions. It follows that continual title maintenance and CBO engagement are crucial in maintaining clean titles.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.263
Teacher spread0.224 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueSurvey ReviewSame topicLand Rights and ReformsFrench-language works237,207