Planting Sounds:re-framing the acoustic environment in 'Tree People' (2014), the story of the Colne Valley Tree Society
Bibliographic record
Abstract
In 1997, I moved to the Colne Valley in West Yorkshire and began volunteering with a local voluntary group, the Colne Valley Tree Society, who plant trees in the valley most winter Saturday mornings. A few years later I began working as a music technology lecturer at the University of Huddersfield and gravitated slowly towards digital filmmaking in my own practice and research with a particular emphasis on documentary film sound. Around 2010 these two activities were brought together as I began filming the activities of the Society for what was to become the documentary film, Tree People. Consequently, I began to explore the history of the Society and the local area itself as I became aware of just how much of a difference their tree-planting activities over many years had made to the valley’s landscape. This article explores the relationship between my own aesthetic ideas and goals in terms of documentary film and its associated digital techniques, especially as it relates to the sonic, the subject matter of the film itself, and the historical and social context of the Society and its location.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".