The Discursive Context of Forest in Land Use Documents
Bibliographic record
Abstract
The term forest can signify many different physical realities. However, discourse analysis of Irish National and European Union forestry-related documents indicates ambiguity around this term is often cultivated rather than clarified. We argue here that policy language often embraces the multiple potential affordances within the term forest as a means of discursively bridging contradictions between economic and conservation goals. While this technique increases the readability and acceptability of such documents by diverse user groups and government bodies, it mutes the on-the-ground tensions of what forests mean for locals. Moreover, cultivating ambiguity favors the status quo through circumventing points of contradiction and shifting the work of interpretation and application of such documents to those on-the-ground, therefore perpetuating existing power differentials. As forests are central to resource management and responses to climate change, addressing this tendency is crucial to finding meaningful and place-specific environmental solutions.
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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) | 0.000 | 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 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".