Private conservation and Indigenous groups with contested identities: how are land trusts navigating recognition politics and controversy?
Bibliographic record
Abstract
New Hampshire has no state or federally recognized Indigenous tribes, and the identities of its self-recognized Abenaki tribes have been publicly contested by Canadian scholars and the Odanak and Wolinak First Nations. As a result, conservation practitioners within New Hampshire land trusts face challenges in making decisions about collaborations, cultural access, and land management involving tribal entities. Our research thus explores how land trusts currently incorporate Indigenous relationships in their operations, and how these practices may be affected by (a) an absence of tribal federal recognition and (b) the publicly contested nature of a tribe. We conducted six semi-structured interviews with conservation staff of land trusts in New Hampshire in the spring of 2024. Results suggest that land trusts in New Hampshire have engaged with contested Abenaki groups by supporting information exchange and educational programs, providing harvesting access, and stewarding culturally significant lands. Land trust staff feel limited in their engagement with Abenaki groups by confining organizational structures, such as governance documents with limited scope and low staff capacity. More significantly, staff feel challenged by confusion surrounding government recognition and public controversy involving Abenaki groups. Staff expressed divergent opinions regarding relationships moving forward with contested Abenaki groups. This study offers context and considerations for the conservation field, where questions of equity and responsibility in the face of Indigenous identity controversy are becoming increasingly nuanced and consequential.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.032 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".