Ecocultural Revitalization: Replenishing Community Connections to the Land
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Disconnection from nature can be caused by physical or psychological separation. The former can be brought about through the physical dislocation of an entire community away from their homelands and to a different environment. Forced resettlement follows from state-directed policies to assimilate indigenous and other marginal groups or from environmental destruction caused by industrial projects. Local communities are rarely consulted and displacement is often against their will (Cernea, 1988, 1997; Cernea and Schmidt-Soltau, 2006). Forced resettlement has occurred through the policies of a large number of colonizing state entities, settler states and dominant populations that have expanded into the territories of indigenous and native peoples over a long period of time (Colson, 1971; Turnbull, 1973; Brody, 1981; Marcus, 1995; Gall, 2002; McKnight, 2002; Samson, 2003; McGrath, 2006; Alfred, 2009). Examples can be seen in Canada, the US, Australia and New Zealand, parts of Asia and Africa including the Kalahari, and throughout Amazonia. Dislocations, both small-and large-scale, stem from natural resource policies on the one hand, advocating the reclamation and expropriation of particular lands and resources, and assimilation campaigns on the other. Assimilation does not necessarily separate communities from their lands physically, but strives to diminish and erode the intrinsic connection with land, spiritually, mentally and emotionally, leading to a form of psychological separation (Samson, 2003; Samson and Pretty, 2006; Pretty, 2007; Alfred, 2009; Albrecht, 2010, this volume).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it