Talking Titler: Evolutionary and Self-Adaptive Land Tenure Information System Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conventional land registration systems often do not produce the desired results in uncertain land tenure situations such as peri-urban areas in developing world cities, post-conflict situations, land restitution claims and aboriginal land systems. In the Talking Titler system, flexibility in creating relationships between people and between people and their interests in land has been the primary design feature. It is a tool for prototyping different designs and for developing land tenure information systems suing evolutionary strategies. The methodology was originally conceived in urban informal settlement upgrade projects and land reform and land restitution projects in South Africa in the 1990’s. In recent years, the concepts have been tested through interviews with aboriginal peoples groups in Canada, field trials and an initial implementation in land regularization in Nigeria, and a land administration study in Somaliland. The paper overviews the conceptual design of the system, how the design was formulated, testing of the system, and current development. The paper concludes by overviewing an initial design and testing with evolutionary database development and self-adapting software using an extensible markup language (XML) database to reduce the human input into system changes as it evolves.
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.
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 | 0.001 | 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 | 0.001 |
| 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 it