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Record W4285464504 · doi:10.32920/ryerson.14647575.v1

An Evaluation of the Subdivision Approval Process in Kingston and St. Andrew, Jamaica

2021· preprint· en· W4285464504 on OpenAlexaff
Leonard Ahijah Francis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTimelineSubdivisionProcess (computing)Investment (military)Political scienceProcess managementPublic administrationBusinessOperations researchComputer scienceEngineeringHistoryLawCivil engineeringArchaeologyPolitics

Abstract

fetched live from OpenAlex

Over the years the development approval process in Jamaica has been criticised by its major stakeholders as being inefficient and costly. It was also seen as a major disincentive in promoting and attracting investment in the Island. In response to the criticisms and investment concerns, successive governments have implemented reforms to improve the process. The purpose of this research is to use the subdivision process in Kingston and St. Andrew as a case study to evaluate the development approval process given these changes, to see if the process has improved. The research uses a mixed method approach to evaluate the process with respect to its effectiveness. The report concludes that the process has improved but is still not meeting the mandated 90 day timeline. The report finishes by making recommendations on how the system can be improved and reformed to meet these time lines.

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)0.045
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.496

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.004
Scholarly communication0.0050.001
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.068
GPT teacher head0.360
Teacher spread0.292 · 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 designObservational
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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