Kohēzija rakstos par ES-Kanādas tirdzniecības līguma noslēgšanu
Bibliographic record
Abstract
Šī bakalaura darba mērķis bija veikt leksiskās kohēzijas analīzi rakstos par ES – Kanādas tirdzniecības līguma noslēgšanu. Pētījuma teorētiskais ietvars balstīts Tanskanena, Saeda, Halideja un Hasanas teorijās par kohēziju, tekstu un tekstveidi, kā arī masu mediju ietekmi uz sabiedrības viedokli. Empiriskai analīzei tika izmantoti raksti par ES – Kanādas tirdzniecības līguma noslēgšanu no The Toronto Star, The National Post un The Globe and Mail. Šajā pētījumā tika izmantotas kvalitatīvās un kvantitatīvās pētījuma metodes. Kvantitatīva metode tika izvēlēta leksiskās kohēzijas līdzekļu statistiskai analīzei, kamēr kvalitatīva metode tika izmantota, lai nodrošinātu ieguto datu tālāku paskaidrojumu. Analīze liecina, ka atkārtošanas kategorijas izmantoto saišu skaits 10 reizes pārsniedz vārdkopas kategorijas skaitu.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.012 |
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".