Treeplanting tales and the knowledge they sow: how experiential knowledge can be shared through narratives and impact forest management in Canada
Bibliographic record
Abstract
The last two summers, I planted over 120 000 trees in British Columbia. Through this experience I gained a tremendous amount of new knowledge. This knowledge is not scientific or propositional, but rather subjective, active, aesthetic, and embodied: it comes from my subjective experience of planting trees through physical suffering and arduous navigation of the logged forest. Philosophers call this knowledge “knowledge by acquaintance” or "experiential knowledge". If its subjective nature makes it difficult to share, I argue that this knowledge can be communicated through narrative arts. My research questions are therefore "How can we share experiential knowledge?” and “What impact can the communication of the tree-planter's experience have on forest management in Canada?” My research objectives are twofold: to explain and demonstrate the value and legitimacy of experiential knowledge as a form of knowledge, and to explore how sharing this knowledge through stories and poetry might impact how foresters and planters do their work. To do so, I will use creative writing to share my experiences as a planter, analysing how this form of writing can help to share knowledge by acquaintance and how it may differ from scientific accounts, as well as exploring the potential impact of sharing these stories with foresters.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.047 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".