Bibliographic record
Abstract
Abstract This paper will reflect on key findings from a Summer 2017 initiative entitled The Role of Culture and Land-Based Healing in Addressing and Ending Violence against Indigenous Women and Two-Spirited People. The Indigenist and decolonizing methodological approach of this work ensured that all research was grounded in experiential and reciprocal ways of learning. Two major findings guide the next phase of this research, complicating the premise that traditional economic activities are healing for Indigenous women and Two-Spirit people. First, the complexities of the mainstream labour force were raised numerous times. Traditional economies are pressured in ongoing ways through exploitative labour practices. Secondly, participants emphasized the importance of attending to the responsibility of nurturing, enriching, and sustaining the wellbeing of soil, water, and original seeds in the process of creating renewal gardens as a healing endeavour. In other words, we have an active role to play in healing the environment and not merely using the environment to heal ourselves. Gardening as research and embodied knowledge was stressed by extreme weather changes including hail in June, 2018, which meant that participants spent as much time talking about the healing of the earth and her systems as the healing of Indigenous women in a context of ongoing colonialism.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".