Bibliographic record
Abstract
Statistical models are valuable tools for informing conservation practices; their outputs often guide policy decisions, identify key driving mechanisms, and shape strategy. Consequently, the demand for accessible methods that produce interpretable results has increased substantially. This thesis addresses the demand from two perspectives. First, to enable identification of sensitive or illegal behaviours which affect biodiversity, we develop a comprehensive framework categorizing all Randomised Response Techniques (RRTs) where the sensitive variable is discrete. We demonstrate how this framework unifies several classical discrete RRT designs, enabling a straightforward comparison of their properties. Building upon this, we introduce a procedure that allows researchers to design surveys optimised for efficiency while meeting specified privacy constraints. To support practical implementation, this work is accompanied by an accessible RShiny application. Second, to aid in identifying common mechanisms of change, we develop a hierarchical clustering framework that aims to balance the minimisation of within-cluster variation with the interpretability of predefined ecological groupings. We apply this framework to a large-scale dataset on Canada's bird populations. Our analysis reveals that traditional guild-level summaries miss substantial species-level variation and proposes several alternative clustering strategies.
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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 | 0.029 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".