Bibliographic record
Abstract
Conservatism and political apologies share a paradoxical relationship, commonly viewed as contradictory, yet upon closer inspection reveal more similarities than expected. This apparent contradiction stems primarily from the varying interpretations and applications of these concepts across different academic disciplines and areas of discourse. Existing interpretations, such as Michael Cunningham's claim that conservatism is more compatible with political apologies than other major western ideologies, are based on broad assumptions. This thesis expands on Cunningham's findings by adopting an intersectional approach to scrutinize the concepts of conservatism and political apologies, in particular within the context of Canadian parliamentary speeches. The thesis answers: are we or are we not able to conceptually locate political apologies within conservatism? Whatever the answer might be to this first question, can we find empirical evidence of that conceptual relationship in a real world context? Guided by the dual enquiries of the conceptual placement of political apologies within conservatism and empirical evidence of this relationship, the research draws upon the morphological analysis of ideologies as proposed by Michel Freeden. The study commences with the exploration of the conservative concept...
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".