Bibliographic record
Abstract
Introduction:The goal of the study was to explore representations of illicit drugs (heroin, cocaine, amphetamine, marijuana) and their users in Polish newspapers. Material and methods:The texts published in four Polish nationwide newspapers between 1.09.2015 and 31.10.2016were analysed.The critical discourse analysis method was used.Theoretical framework was provided by Foucault's concept of power strategies (e.g.discipline, biopolitics, governmentality) linked with press discourses within the discursive-institutional structure called "apparatus".Results: Amphetamine was highly criminalised.Cocaine tended to be criminalised, but also linked with celebrities, elites.Heroin was constructed as drug causing death or degradation.Marijuana was depicted as an illicit drug Streszczenie Wprowadzenie: Celem pracy było zbadanie reprezentacji czterech narkotyków (heroina, kokaina, amfetamina, marihuana) i ich użytkowników w polskiej prasie.Materiał i metody: Zbadano teksty opublikowane w czterech ogólnopolskich dziennikach w okresie 1.09.2015-31.10.2016r.Wykorzystano metodę krytycznej analizy dyskursu.Ramy teoretyczne tworzyła koncepcja Foucaulta dotycząca różnych strategii władzy (np.dyscypliny, biopolityki, rządomyślności), które są powiązane z dyskursami prasy przez językowo-instytucjonalną strukturę zwaną "urządzeniem".Wyniki: Amfetamina podlegała silnej kryminalizacji.Kokaina była również wiązana z przestępczością, aczkolwiek pojawiały się tu konotacje celebryckie i elitarne.Heroinę konstruowano jako narkotyk powodujący śmierć lub degradację.Natomiast ma-The media focusing on crime and negative problems enhance power discourses related to drug criminalisation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".