Bibliographic record
Abstract
In this diploma I will analyse in detail several selected poems by one of the world’s most acknowledged and famous artists. Leonard Cohen is a Canadian poet, novelist, songwriter and singer who has written novels, countless poems and lyrics that have been published in his poem collections and albums. In his life, he has won many awards, titles and honours. On the other hand, there is much more to this man than these achievements. One of the reasons he has maintained his position amongst the best is the themes he writes about. Personal relationships, separation, sexuality and isolation are a few of the reflections in his works. Other keynotes to his poems are religion and spirituality. He was born in a Jewish family, but that did not stop him from experimenting and studying other religions. He has been especially keen to Buddhism and Christianity, which he often refers to in his poems and songs. Furthermore, a number of times his songs are socio-politically oriented. He has expressed his views and disagreements on current events through his lyrics where he has been quite sharp and scornful. He has experimented with different musical instruments and has worked with different artists on his projects, which is a proof of his continuous development and searching.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".