Bibliographic record
Abstract
This paper is a personal reflection about ‘learning labour’ within the context of a twenty-year relationship with my late partner, Mary-Jo Nadeau (1965–2021). As an academic, self-identified labour geographer, I give recognition to a number of lessons that I learned from Nadeau, herself a feminist sociologist, anti-racist activist, and labour organizer. The paper borrows from a largely feminist inspired literature on academic relationships and how such relationships influence intellectual development and pursuits. The paper explores a number of questions including: How do these relationships work? Do they increase professional success? What is the intellectual impact on each other’s work, even if you do not write together? And also important, what are the effects of gender and other relations of power in such a relationship? The paper concludes that reflection upon engagements with intimate partners is something that geographers and other scholars should be more open to. Further such reflections must go beyond mere acknowledgement of the intellectual contributions of those who are too often rendered invisible in research processes to how such intimacies shape research and scholarship.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.040 | 0.067 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".