Bibliographic record
Abstract
“Old Trees Are Our Parents”: Old Growth, New Kin, Forest Time We are aged by culture, as Margaret Gullette has perfectly put it, her emphasis placed on the negative associations sutured to being old in capitalist societies. What would it mean to be aged by trees? To grow old with trees as our companion species? To understand that “old trees are our parents,” embracing the knowledge that we humans share a lineage with trees? I approach these questions through the prism of the magisterial novel The Overstory (2018) by the American writer Richard Powers, singling out three scenes that offer parables of post-human aging: first, humans humbled in comparison with trees in terms of longevity; second, a new understanding of what constitutes the genetic lifeworld of Homo sapiens; third, deep knowledge of the green world on the part of humans who have learned across their lifetimes and into their seventies to embrace the wisdom of trees. If the first scene calls up feelings of awe, including the sublime, the second engenders feelings of family and kinship across species, and the third, the consolations offered by the guidance of trees, developed over the long evolutionary temporality of forest time. Forest time: the timescale, or agescale, of the life and death of trees mediates the timescales of geological long time, the emergence of life on the planet, the time of human history, and the life span of Homo sapiens. I focus on four of the major characters who, some seventy years old at the end of the novel, exemplify old growth, simultaneously feeling they belong to a forest world that is both vital and old, a sanctuary, and envisioning a regreening of the planet that is in grievous peril of being stripped of its forests. Methodologically this essay is an experiment in multi-species literary ethnography through close reading of a single contemporary novel, which has had an extraordinary impact, and in the context of recent transformative research on trees. The evocative phrase “old trees are our parents” comes from the nineteenth-century American naturalist and philosopher Henry David Thoreau, suggesting a literary lineage as well as a genetic lineage across species—humans and trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| 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".