Bibliographic record
Abstract
Seasons of a River is a fictional story about a woman looking back on her life. In her recollections she has to acknowledge and come to terms with the fact that her relationships with indigenous people in Canada have been inequitable and unbalanced. She realizes that she has been in the role of helper, of ally, and as such has perpetuated ideas of colonization and oppression, despite her good intentions. The protagonist, Annan, is in the process of trying to understand the complicated issues involved with the helping industry and her part within that industry. My introduction discusses the power differentials within allyship relations. I discuss how liberalism, at the root of the helping industry, although seen as a positive ideology, often perpetuates inequities and injustices when dealing with groups that are seen as “different.” I also discuss the importance of relationship building and listening and the need for quelling the tendency to assist others without consultation. This introduction and story also contain themes of ageism and how these attitudes are woven into the fabric of society in such a way as to internalize oppression and the idea of staying in one’s “place.” Ultimately, this story is about transformation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".