A missing piece of the puzzle of on-farm freshwater restoration: What motivates land managers to record and report land management actions?
Bibliographic record
Abstract
Worldwide, progress has been made toward managing productive lands more sustainably to improve freshwater health. However, a lack of national guidance for environmental reporting and recording means that it is not possible to quantify consistently which land management actions that help improve water quality have been implemented, where, when, and to what extent. This situation suggests that information on the effectiveness of these actions is missing or fragmented. Systematic recording and reporting of land management actions is an important piece of a large freshwater restoration puzzle. We investigated what motivates New Zealand land managers to record their actions and report them to their networks by conducting 23 semi-structured interviews. Between February and November 2020, we spoke with food producers, New Zealand Indigenous people of the land <em>tāngata whenua</em> community members, and government and industry representatives. The key themes that described motivators for these land managers to record and report land management actions were collective engagement (e.g., working with catchment care groups), identity and social norms (e.g., being a “socially approved” farmer), and efficient farm management (e.g., using one simple recording tool for multiple purposes to save time). While these findings will be broadly germane to international contexts, they are being used specifically to inform the development of a proposed National Register of Land Management Actions in Aotearoa New Zealand.
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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 | 0.038 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".