Center for Land Tenure Studies 2022 Annual Report
Bibliographic record
Abstract
The Centre for Land Tenure Studies (CLTS) published in 11 working papers and 14 peer reviewed journal articles. The published worked covered issues around land tenure policies, land markets, housing markets, environmental reliance, and REDD+, environmental reliance, climate smart technologies, risk and time preferences, trust and social preferences. The CLTS papers and reports are accessible through the CLTS webpage, the Scandinavian Working Papers in Economics (S-WoPEc), the library (Brage) of the Norwegian University of Life Sciences (NMBU), and the Land Portal webpage (see: https://landportal.org/organization/centre-land-tenure-studies). In addition to these access points, Econstor also started registering and distributing information about the CLTS publications in 2021 (see: https://www.econstor.eu/handle/10419/242695). The CLTS also published three books in print. Statistics on visibility show that on average, people viewed the abstracts 39 times in 2022 compared to the 92 views in 2021, which represents a 57 percent drop between the years. However, in 2022 there were more views in the first quarter of the year compared to the last quarter following upload of papers. For paper downloads, the centre achieved a 37 percent increase, particularly in the last quarter of the year. The long-term trend shows unique patterns within the years since 2012. There are also different spikes within the years. However, we should note that much of the distribution of the CLTS WPs also goes through ResearchGate as authors upload the papers there. The faculties working on CLTS related work also reported several activities. The School of Economics and Business graduated one PhD student, who worked on land related studies. The School was also involved in several projects supported by the Research Council of Norway and NORHED II. In LANDSAM, the Department of Property and Law is involved in projects focusing on (1) rules, norms, and cooperation in outlying grazing areas, and (2) the Planning and Building Act between demand, land policy and sustainability. The Department of Property and Law also graduated one PhD who worked on "Cadastral records of cases conducted by the land consolidation court". Again, under LANDSAM, Noragric and Byreg departments collaborated on an NFR research application focusing on sustainable recreation around the Oslo Fjord. In MINA, the Department of Ecology and Natural Resource Management worked on a collaborative project that represents a multidisciplinary approach to diagnosing and improving policy and management decisions for dry forestland and resources in the face of climate change. Two PhD students are working in the project since August 2021.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.209 | 0.197 |
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 source (direct Gemma or distilled Codex), 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".