Bibliographic record
Abstract
introductionCanada is world renowned for its public health system and health promotion policies, but the everyday realities of healthcare can poorly match such endorsements (Raphael et al., 2016).Shaped by colonising histories, racialised people(s) continue to experience racism and inequity in and outside of healthcare settings, affecting their well-being (Hassen et al., 2021, p. 2; Gebhard, McLean and St. Denis, 2022;Geronimus, 2023).In this short piece, I present three vignettes that focus on my father and me in different spaces.In these vignettes, he is dying of cancer, and I am observing, listening to and conversing with him.My father was one of eight children who grew up in a small village near the city of Hoshiapur, part of the Indian state of Punjab.After his schooling, he was accepted into Marine College in Mumbai.His work as a marine engineer led him to migrate to Canada in the late 1960s.There he married my mother, and they had three children while working in their respective professions until retirement.My father loved cars and sport and was captain of his local cricket team, a group of diasporic friends from the Caribbean and India.The vignettes took place five years ago, when I travelled to Canada to be with him in his last days.In them, I connect feminist and race theory with lived experience to capture three interconnected moments of racialisation and illness that take place in a hospital, in a garden centre and on a bus (Ramazanoglu and Holland, 2002).Through these moments, I highlight the banality of racialisation that can demean and intensify the embodiment of life-changing illness in the everyday.the hospital I waited with my father in a hospital room that had a bed and a big window through which the midafternoon daylight shone.We sat near the door on two chairs side by side.It was quiet.The specialist responsible for a new trial of cancer treatment entered with a clipboard and sat down across from him on a short stool with wheels.She was a white woman dressed in a white lab coat, loose navy trousers and sneakers.She asked my father some questions.He responded, but he chose to say more about his recent excruciating experiences of receiving radiation and if it was all worth it.She replied to his comments, saying, 'you have a chance to be at the forefront of medicine and to help make a difference'.He sank back slightly in his chair, shamed and glum in response to her tone, infantilising, glib and routine.Her 1245925F ER0010.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".