ELD Initiative: Practitioner’s Guide
Bibliographic record
Abstract
As the world’s population continues to rise, there is an ever increasing demand for our land to produce a diverse range of products such as food, timber, and fuel. Our growing need for these goods is leading to higher levels of competition between different land uses and, as a result, land users. Not only is the quantity of land available for production under current technical and economic conditions limited, but there is also growing evidence that the quality of our land is degrading (Safriel, U. N. 2007; Millennium Ecosystem Assessment, 2005; TEEB, 2010). As a result, healthy land that is available for production is becoming an increasingly scarce resource, and there is a great need to make better use of what we have available, both now and in the future. Improved co-production of knowledge is needed between scientists, local community members, technical advisors, administrators and policy makers. These different groups may be considered “stakeholders”, defined as those who are affected by or who can affect a decision or issue (Freeman, 1984). Stakeholder engagement can be defined as “a process where individuals, groups and organisations choose to take an active role in making decisions that affect them” (Reed, 2008). It is argued that stakeholder engagement may enhance the robustness of policy decisions designed to reduce the vulnerability of ecosystems and human populations to land degradation (de Vente et al., in press). In this way, it may be possible to develop response options that are more appropriate to the needs of local communities and can protect their livelihoods and wellbeing (ibid).
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 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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.415 | 0.302 |
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".