Bibliographic record
Abstract
This thesis sets out a new form of political analysis which could be referred to as materialist holism.Materialist holism uses the multidisciplinary study of inequality and of the social determinants of health to draw an empirical link between Marx's theory of alienation and a critique of totality.Using this analysis I make the case for a renewed and transformed social democracy, which could be called diagonalist social democracy.My partner Elanor Sherlock who has been infinitely patient with me as I have spent so much of my time during the writing of this thesis being busy and stressed, and our roommates the Moonfruits who have also put up with me in this state-all not only support me in my political pursuits and activism, but stand side by side with me as a political family ('local 518').My mother who supported me through the rough patches growing up and who, after I barely made it through high school, regularly encouraged me to pursue post-secondary education, even after I dropped out of college, until I finally succeeded.My father, who also supported me and who introduced me to the political polemic at the family dinner table.My brother Geoff, whose socialism has inspired me more than anyone else's, and whose conversation I would be lost without.My friend, teacher and fellow diagonalist Keith Haysom whose courses (and perspectives) on Marxism, critical theory, and social movements helped strengthen my political positions and deepen my perspectives.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".