Evolutionary Land Tenure Information System Development: The Talking Titler Methodology 1
Bibliographic record
Abstract
SUMMARY 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 cases and aboriginal lands. The Talking Titler system is a software design and development methodology where flexibility in creating relationships between people and between people and their interests in land has been the primary design feature. It uses a range of structured, unstructured and semi-structured data types as evidence such as videos, photographs, documents, maps, digital graphics and sound recordings. It is most suited as a tool for prototyping and for evolutionary land tenure information system design and implementation. In its current form it is also a good training and information design support tool, and it can be used as a local level land records system. 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 and field trials in Nigeria. This paper provides an overview of the conceptual design of the system, how the design was formulated, testing of the system, and current development. The current version uses a conventional relational database design. The research team has experimented with evolutionary database development using extensible markup language (XML) databases and self adapting software to reduce the human input into system changes. A current project is examining how to evolve the database using social network analysis.
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 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) | 0.001 | 0.001 |
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.
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".