Transparency of tenure institution as a factor in ensuring equity in practice: The case of stool lands privatization and formalization as traditional leasehold properties in Ghana
Bibliographic record
Abstract
The 1992 Constitution of Ghana outlawed the creation of freehold interest in stool lands and thus formalized lands acquired for residential and commercial use as traditional leasehold properties. As a consequence, anyone who owns a residential property on stool land is a leaseholder and, upon expiration of the lease, must meet certain covenants and conditions for renewal or surrender the property, including any improvements thereon. Given the importance of transparency, equity, and security around the terms of leasehold tenure and enforcement mechanisms, this paper addresses three substantive questions. The first concerns whether lessees are aware of these institutional arrangements and are willing to comply with the terms and conditions of the lease. The second question probes whether increased awareness or knowledge of the leasehold tenure and its requirements would increase the willingness to renew the lease or surrender the property peacefully when the lease expires. The third question addresses how much lessees can afford or are willing to pay for renewal. Using a one-group pretest-posttest experimental design and data from 420 respondents, drawn from communities across Greater Kumasi (GKMA), this study shows that many lessees have limited or no institutional knowledge of residential leaseholds. Consequently, they are unwilling to renew their lease or surrender the property when the lease expires. The research findings, however, indicate that lease renewals could be increased significantly, from 35.24 % to 84.05 %, with increased knowledge and transparency. The findings further suggest that surrendering the property to stools is not an option for lessees. Hence the study recommends that traditional authorities ensure equity in leasehold practice by making the lease creation process more transparent, including instituting an official payment plan, with a fee of not >10 % of the property's market value for renewals. • The study examines lessees’ knowledge and willingness to comply with traditional leasehold terms. • Lessees’ lack of institutional knowledge leads to non-compliance with lease terms. • Lessees are unwilling to renew and surrender their properties upon the lease expiry. • Lease renewal could however increase significantly from 35.2% to 84.1% via a transparent lease creation process and education. • The study contributes to understanding the importance of transparency in land tenure reform.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".