Ecopsychology for Sustainable Relationships: A Review of Reddick's <i>The Same River</i>
Bibliographic record
Abstract
Now is a historical time wrapped with grief. We face the loss of ecology: our relationships to one another and our natural environment are strained, disrupted, severed, and brutalized rather than reciprocal, mutual, and just. Historically and today, globally, the pain cuts most deep for many black, indigenous, and people of color. We all need healing, hope, and action. What role does Ecopsychology play in understanding this grief, supporting the healing, and journeying the road to justice? We employ Lisa Reddick's book The Same River to discuss relationship and interconnection through the lens of representation, voice, conflict, resolution, hegemony, and ecology. Ultimately, Reddick's story is a call for hope. By attuning to each other, and to the earth, we turn toward a future comprising sustainable relationships. Fostering sustainable relationships requires many things. We must dive deep into the pain and suffering our severed relationships have caused. We must heal, through listening, empathy, trust, and love. We must meet in moments of mutuality, respect, and regard, with each other, and the more-than-human natural world. In The Same River, Reddick illustrates such a path.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".