Debbie Lee and Kathryn Newfont.</strong> <em>The Land Speaks: New Voices at the Intersection of Oral and Environmental History</em>.
Bibliographic record
Abstract
In his essay “The Land Ethic,” conservationist and writer Aldo Leopold advocates for changing “the role of Homo sapiens from conqueror of the land-community to plain member and citizen of it” (173). The Land Speaks challenges readers to not only adopt this land ethic, but to practice it by listening to the land and acknowledging its agency. Authors and editors Debbie Lee and Kathryn Newfont argue that oral history can be used as a tool across fields, not just within the humanities or archival studies, to examine human relationships with the land. Adopting this tool comes with three challenges. First, oral historians need to acknowledge that “the land itself speaks”(10). Second, there are people who can “hear, understand, and translate into human language messages from the land” (10). Third, historians must recognize “that wildlife and wildlands have been marginalized and denied voice in ways that parallel the human disenfranchisement” (12). From national forests to urban landscapes, the fourteen essays in this work address these challenges and demonstrate that it is possible to record the land’s story through the oral histories of voices we would not otherwise hear. As the land speaks, it does so through the voices of indigenous peoples, hunters, firefighters, housewives, and park rangers. These voices make the work a compelling read and inspire one to discover how the land speaks in their oral history archives.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".