Bibliographic record
Abstract
The African proverb, “it takes a village to raise a child” captures the essence of how I made sense of my world as a child. This self-reflexive auto-ethnography explores my experiences with other-mothering as a child born to an unwed mother in Toronto, Canada in the early 1970s. After my birth, I was sent to live on the Caribbean islands of Trinidad and Tobago with my mother’s family. My grandparents assumed the role of my parents and their daughters shared in my upbringing at different points in my life. As a child, I knew that my circumstance was unique because it was clear that my cousins lived in two parent households. Growing up knowing that my biological mother lived in Canada shaped the ways in which I understood my relationships and my status as “quasi-orphan” and displaced, albeit in a context that provided a solid foundation for my sense of self and identity. Using Collins’ (2000) concept of other-mothering, I discuss how my grandmother’s death in 2013 deemed me motherless. I share how we prepared for her funeral as a family and the out-of-body experience I had on the day of her burial. Today, I deal with her loss by forging ties with my aunts, who deem me, at once, niece and sister. I consider the extent to which her loss has solidified my place in our family as I work to fill the void that her passing has created.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.028 |
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".