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Record W2900255899 · doi:10.5860/rbm.19.2.156

Debbie Lee and Kathryn Newfont.</strong> <em>The Land Speaks: New Voices at the Intersection of Oral and Environmental History</em>.

2018· article· en· W2900255899 on OpenAlexaff
Jillian Sparks

Bibliographic record

VenueRBM A Journal of Rare Books Manuscripts and Cultural Heritage · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsOral historyIndigenousEnvironmental ethicsSociologyActive listeningWildlifeAgency (philosophy)HistoryMedia studiesAnthropologySocial scienceEcology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.202
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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