“The purpose of life is finding the largest burden that you can bear and bearing it.” : A study of the making of meaning among Jordan Peterson supporters
Bibliographic record
Abstract
The aim of this thesis is to study, understand and explain the theories and work of the Canadian psychologist Jordan Peterson, whose controversial statements and lectures have made him a darling of certain factions of the political right, as he portrays himself as an enemy of progressive ideology. With a focus on understanding and explaining Peterson and how he provides meaning to his followers, the study will go through Peterson’s work in his two books Maps of Meaning and 12 Rules for Life in order to analyze their content and the follower’s reaction to the books as well as Peterson’s persona as a whole. To analyze these works, hermeneutic methods based on the work of finnish theologian Björn Vikström will be utilized. The texts will be analyzed on a textual and intertextual level, but the role of the author as well as the readers will also be put under scrutiny in order to elaborate on many aspects of Peterson’s writing. To analyze how he provides meaning to his followers and the definition of the terms lifestance and meaning, the work of Swedish theologian Carl Reinhold Bråkenhielm will be referenced and compared to Peterson’s work. While Vikström and Bråkenhielm will be the main sources of intertextual comparison with Jordan Peterson, they will also be supplemented with the work of other established theologians such as Hjalmar Sundén and others to further understand and compare the making of meaning undertaken by Jordan Peterson to other academic studies in the field of making meaning.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".