The changing relationships between forestry and the local community in rural northwestern IrelandAn earlier version of this paper was presented at the IUFRO 3.08 conference “Small-scale Forestry and Rural Development,” 18–23 June 2006, Galway, Ireland.
Bibliographic record
Abstract
Following centuries of deforestation, Ireland has undergone a substantial afforestation programme in the last 40 years. This paper presents the results of a case study undertaken to examine local response to afforestation. The study is set in Arigna, a region in northwestern Ireland that has traditionally depended on agriculture but has experienced relatively high rates of afforestation in recent decades. Relying on documentary evidence and in-depth qualitative interviews conducted with local stakeholders, the results suggest more local resistance to afforestation than one might expect in a country that has historically experienced such massive deforestation. Among the reasons uncovered for this resistance is the history of land tenure in rural Ireland, the institutional means by which afforestation has been conducted, the tree species used, and the aesthetic appearance of the forest stands once established. Underlying all of this is an apparently widespread local perception that forestry has benefited outsiders more than locals. Yet, the study also documents local perceptions that those responsible for afforestation have responded to concerns and that resistance to afforestation may be declining, as well as the reasons for this decline. The paper concludes with a discussion of the importance of local history and community involvement in developing socially acceptable forestry.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".