The Cserénfa experiment.
Bibliographic record
Abstract
The Cserénfa experiment. On the attempt to install computers and Internet in a tiny village in Hungary Authors: György Lengyel, professor of sociology, Corvinus University of Budapest, Department of Sociology and Social Policy, gyorgy.lengyel@uni-corvinus.hu Eliza Eranusz, Ph.D. student, Corvinus University of Budapest Department of Sociology and Social Policy Dániel Füleki, junior research fellow, Corvinus University of Budapest Department of eBusiness László Lőrincz, Ph.D. student, Corvinus University of Budapest Department of Sociology and Social Policy Viktória Siklós, Ph.D. student, Corvinus University of Budapest Department of Sociology and Social Policy Abstract The research aimed at studying social impacts of network-based and organized IT-learning in a local community. At the beginning of the project courses were organized, then computers and Internet accesses were made available for families, and we asked them to provide help for others on a voluntary basis. The turnout of the village Telehouse (the public eAccess point) was monitored at the same time. Focus groups, interviews, computer generated documents and diaries of the participants were the major sources of the research. As for the primary impacts, by the end of the experiment IT knowledge and related aspirations increased although side effects, like envy and frustration also did occur. The Internet activity of the participants was predominated by recreation and not by information gathering or resource extension. As far as wider social aspects are concerned, at the beginning of the project villagers thought that people were reserved, mistrustful and unprepared to help, and this mood did not change much to the end. While children learn basic IT skills in the elementary school adults are worse off in this respect. Therefore organizing courses for them was popular and proved to be useful. Another policy result is that outplacing to networks and the Telehouse satisfy more or less the same potential demand thus it is not advisable to apply both of them in a small settlement. Word count: app. 7500
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.013 |
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".