Eesti kirjanduselu Torontos
Bibliographic record
Abstract
Bakalaureusetöö eesmärk on kirjeldada läänediasporaa ühe suurima eestlaste keskuse, Toronto, kirjanduslikku tegevust alates 1950. aastast tänapäevani. Uurimuses keskendutakse peamiselt ühiskondlikule küljele ja proovitakse välja selgitada Toronto \nkirjanduseluga seotud organisatsioonid, eestvedajad, kirjanduslikud nähtused ja kõige \nselle mõju eestluse säilitamisele. \nBakalaureusetöö koosneb seitsmest peatükist. Esmalt vaadeldakse üldiselt \npagulaselu ning eksiilkirjandusega kaasnevaid nähtuseid. Antakse ka ülevaade Kanada eesti kirjanduselust ja seal tegutsenud kirjanikest. Seda võib näha teemasse \nsissejuhatusena, seitsmendat ehk viimast osa aga eelnevaid peatükke kinnitavana. \nViimases peatükis on proovitud kultuuritegelase, Hannes Oja, kaudu näidata üksikisiku \npanust, katsumusi ja õnnestumisi üpriski väikeses kogukonnas. Ülejäänud viies peatükis käsitletakse 1950-ndaid, 1960-ndaid, 1970-ndaid, 1980-ndaid ning kirjanduselu alates Eesti taasiseseisvumisest tänapäevani.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.425 | 0.106 |
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".