Frihet under ansvar eller sann och opåverkad kunskap – Trovärdighet hos källor och hur den bedöms av två användargrupper.
Bibliographic record
Abstract
This essay is investigating what type of sources we trust, depending on how the information in these sources is created and provided. To exemplify, the sources Wikipedia and Nationalencyklopedin (the Swedish National Encyclopedia) were used, each one representing different approaches. The first is a representation of the collective intelligence, where anyone can contribute with their knowledge to the public – freedom under responsibility. The latter represents peer-reviewed knowledge, desirable enough to have people paying for it– true and unaffected.\nBased on theories of source criticism, collective intelligence, expertise and the different generations of digital natives and digital immigrants, two focus groups were conducted and analysed. A group of upper secondary school students and a group of retirees were presented with encoded texts from both encyclopedias, which they analysed critically from their opinion on reliability and objectivity. Their reasoning shows a well demonstrated awareness of the need of a critical reasoning and information literacy when dealing with different sources. But the question remained, particularly in regards to the students, if they do after all practice these skills in their information retrieval.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".